2025

Journal articles

Ciesielski D.K., Y. Li, S. Hu, E. King, J.F. Corbey, and P. Stinis. 2025. Deep operator network surrogate for phase-field modeling of metal grain growth during solidification. Computational Materials Science 246, no. _:Art. No. 113417.PNNL-SA-198433.doi:10.1016/j.commatsci.2024.113417

2024

Journal articles

Munikoti S., D. Agarwal, L. Das, M. Halappanavar, and B. Natarajan. 2024. Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications. IEEE Transactions on Neural Networks and Learning Systems 35, no. 11:15051 - 15071.PNNL-SA-174409.doi:10.1109/TNNLS.2023.3283523

Sun W., A. Li, S. Stuijk, and H. Corporaal. 2024. How much can we gain from Tensor Kernel Fusion on GPUs?. IEEE Access 12, no. _:126135 - 126144.PNNL-SA-186332.doi:10.1109/ACCESS.2024.3411473

Liu Z., M. Ng, S. Srivastava, T. Li, J. Liu, T. Phu, and B. Mateescu, et al. 2024. Label-Free Single-Vesicle Based Surface Enhanced Raman Spectroscopy: A Robust Approach for Investigating the Biomolecular Composition of Small Extracellular Vesicles. PLoS One 19, no. 6:Art No. e0305418.PNNL-SA-176811.doi:10.1371/journal.pone.0305418

Ashtari Esfahani A., S. Boser, N. Buzinsky, M.C. Carmona-Benitez, C. Claessens, L. De Viveiros, and M. Fertl, et al. 2024. Real-time Signal Detection for Cyclotron Radiation Emission Spectroscopy Measurements using Antenna Arrays. Journal of Instrumentation 19, no. 05:Art. No. P05073.PNNL-SA-192866.doi:10.1088/1748-0221/19/05/P05073

Bassetti S., B.J. Hutchinson, C. Tebaldi, and B. Kravitz. 2024. DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models. Journal of Advances in Modeling Earth Systems 16, no. 10:e2023MS004194.PNNL-SA-193631.doi:10.1029/2023MS004194

Thomas D.G., Z.J. Weems, R.E. Overstreet, and B.A. Wilson. 2024. Early Inference of Nuclear Technology-Directed Research Activities of Authors from Scientific Publications. ESARDA Bulletin 66, no. _:7 - 27.PNNL-SA-192556.doi:10.3011/ESARDA.IJNSNP.2024.2

Sweeney A.J., Q. Fu, S. Po-Chedley, H. Wang, and M. Wang. 2024. Unique Temperature Trend Pattern Associated with Internally Driven Global Cooling and Arctic Warming during 1980-2022. Geophysical Research Letters 51, no. 11:e2024GL108798.PNNL-SA-195081.doi:10.1029/2024GL108798

Hossain R., M. Gautam, J. Olowolaju, H. Livani, and M. Ben-Idris. 2024. Multi-Agent Voltage Control in Distribution Systems using GAN-DRL-based Approach. Electric Power Systems Research 234, no. _:Art No. 110528.PNNL-SA-195602.doi:10.1016/j.epsr.2024.110528

Sun A., P. Jiang, P. Shuai, and X. Chen. 2024. Bridging hydrological ensemble simulation and learning using deep neural operators. Water Resources Research 60, no. 10:Art. No. e2024WR037555.PNNL-SA-203681.doi:10.1029/2024WR037555

Wang J., E. Hendricks, C. Rozoff, M. Churchfield, L. Zhu, S. Feng, and W. Pringle, et al. 2024. Modeling and Observations of North Atlantic Cyclones: Implications for U.S. Offshore Wind Energy. Journal of Renewable and Sustainable Energy 16, no. 5:052702.PNNL-SA-197059.doi:10.1063/5.0214806

Allec S.I., X. Lu, D.R. Cassar, X.T. Nguyen, V.I. Hegde, T.S. Mahadevan, and M. Peterson, et al. 2024. Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability. Journal of the American Ceramic Society 107, no. 12:7784-7799.PNNL-SA-196287.doi:10.1111/jace.19937

Mariotti A., D.C. Bader, S.E. Bauer, S.E. Bauer, G. Danabasoglu, J.P. Dunne, and B. Gross, et al. 2024. Envisioning U.S. Climate Predictions and Projections to Meet New Challenges. Earth's Future 12, no. 6:Art. No. e2023EF004187.PNNL-SA-199872.doi:10.1029/2023EF004187

Horawalavithana Y.S., E.M. Ayton, A.A. Usenko, R.J. Cosbey, and S. Volkova. 2024. Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers. IEEE Transactions on Computational Social Systems 11, no. 5:6982 - 7001.PNNL-SA-181649.doi:10.1109/TCSS.2024.3416837

Zahura F., G. Bisht, Z. Li, S. McKnight, and X. Chen. 2024. Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Random Forest. Ecological Informatics 82, no. _:Art. No. 102757.PNNL-SA-191894.doi:10.1016/j.ecoinf.2024.102757

Kelliher J., Y. Xu, M.C. Flynn, M. Babinski, S. Canon, E. Cavanna, and A. Clum, et al. 2024. Standardized and accessible multi-omics bioinformatics workflows through the NMDC EDGE resource. Computational and Structural Biotechnology Journal 23, no. _:3575-3583.PNNL-SA-200312.doi:10.1016/j.csbj.2024.09.018

Rahman A., N. Lata, B.G. Sebben, D.N. Dexheimer, Z. Cheng, R. Godoi, and A. Bilbao, et al. 2024. Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single Particle Measurement. ACS ES&T Engineering 4, no. 10:2393–2402.PNNL-SA-194864.doi:10.1021/acsestengg.4c00262

Zhuang B., B. Gencturk, A. Oberai, H. Ramaswamy, R.M. Meyer, A.S. Sinkov, and M.S. Good. 2024. Impurity Gas Detection for SNF Canisters Using Probabilistic Deep Learning and Acoustic Sensing. Measurement Science & Technology 35, no. 12:Art No. 126005.PNNL-SA-190765.doi:10.1088/1361-6501/ad730d

Nguyen J.H., R.E. Overstreet, E. King, and D.K. Ciesielski. 2024. Advancing the Prediction of MS/MS Spectra using Machine Learning. Journal of the American Society for Mass Spectrometry 35, no. 10:2256-2266.PNNL-SA-197287.doi:10.1021/jasms.4c00154

Graham E.B., V.A. Garayburu-Caruso, R. Wu, J. Zheng, R.S. McClure, and G. Jones. 2024. Genomic fingerprints of the world's soil ecosystems. mSystems 9, no. 6:Art. No. e01112-23.PNNL-SA-191207.doi:10.1128/msystems.01112-23

Shaikh S., M. Taufique, K. Balusu, S.S. Kulkarni, F. Hale, J.K. Oleson, and R. Devanathan, et al. 2024. Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-based Battery Enclosures for Crashworthiness. Applied Composite Materials 31, no. 5:1475–1493.PNNL-SA-184696.doi:10.1007/s10443-024-10218-z

Mukherjee R., F. Policelli, R. Wang, E. Arellano-Thompson, B. Tellman, P. Sharma, and Z. Zhang, et al. 2024. A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps. Earth System Science Data 16, no. 9:4311–4323.PNNL-SA-204138.doi:10.5194/essd-16-4311-2024

Schneider S., S.H. Kramer, S. Bernstein, S. Terrill, D. Ainley, and S. Matzner. 2024. Autonomous thermal tracking reveals spatiotemporal patterns of seabird activity relevant to interactions with floating offshore wind facilities. Frontiers in Marine Science 11, no. _:Art. No. 1346758.PNNL-SA-196797.doi:10.3389/fmars.2024.1346758

Geller-Mcgrath D.E., K. Konwar, V. Edgcomb, M. Pachiadaki, J. Roddy, T. Wheeler, and J.E. McDermott. 2024. Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict. eLife 13, no. _:Art No. e85749.PNNL-SA-197656.doi:10.7554/eLife.85749

Hegde V.I., M. Peterson, S.I. Allec, X. Lu, T.S. Mahadevan, X.T. Nguyen, and J. Kalahe, et al. 2024. Towards Informatics-Driven Design of Novel Nuclear Waste Forms: A Perspective. Digital Discovery 3, no. 8:1450-1466.PNNL-SA-193635.doi:10.1039/D4DD00096J

Zhang S., B.E. Harrop, L. Leung, A. Charalampopoulos, B. Barthel Sorensen, W. Xu, and T. Sapsis. 2024. A Machine Learning Bias Correction of Large-scale Environment of High-Impact Weather Systems in E3SM Atmosphere Model. Journal of Advances in Modeling Earth Systems 16, no. 8:e2023MS004138.PNNL-SA-192555.doi:10.1029/2023MS004138

Ahmmed B., E. Rau, M. Mudunuru, S. Karra, J.R. Tempelman, A.J. Wachtor, and J.B. Forien, et al. 2024. Deep Learning with Mixup Augmentation for Improved Pore Detection during Additive Manufacturing. Scientific Reports 14, no. _:Art. NO. 13365.PNNL-SA-198405.doi:10.1038/s41598-024-63288-1

Eshun J., N.C. Lamar, S.G. Aksoy, S.M. Akers, B.J. Garcia, H.S. Cunningham, and G. Chin, et al. 2024. Identifying Sample Provenance from SEM/EDS Automated Particle Analysis via Few-shot Learning coupled with Similarity Graph Clustering. Microscopy and Microanalysis 30, no. 4:Art. No. ozae068.PNNL-SA-191238.doi:10.1093/mam/ozae068

Cambron T., J.M. Deines, B. Lopez, R. Patel, S. Liang, and D.B. Lobell. 2024. Further adoption of conservation tillage can increase maize yields in the western US Corn Belt. Environmental Research Letters 19, no. 5:Art. No. 054040.PNNL-SA-194139.doi:10.1088/1748-9326/ad3f32

Roccapriore K., M.A. Ziatdinov, A. Lupini, A.P. Singh, U. Philipose, and S.V. Kalinin. 2024. Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels. Journal of Applied Physics 135, no. 11:Art. No. 114303.PNNL-SA-201685.doi:10.1063/5.0193607

Hollenbach J., C.M. Pate, H. Jia, J.L. Hart, P. Clancy, and M.L. Taheri. 2024. Real-Time Tracking of Structural Evolution in 2D MXenes using Theory-Enhanced Machine Learning. Scientific Reports 14, no. _:Art. No. 17881.PNNL-SA-200076.doi:10.1038/s41598-024-66902-4

Zhang J., Y. Men, L. Ding, X. Lu, and W. Du. 2024. Gray-Box Modeling for Distribution Systems with Inverter-Based Resources: Integrating Physics-Based and Data-Driven Approaches. IEEE Transactions on Industry Applications 60, no. 4:5490-5498.PNNL-SA-193812.doi:10.1109/TIA.2024.3392710

Jiang P., P. Shuai, A. Sun, and X. Chen. 2024. Optimizing Parameter Learning and Calibration in an Integrated Hydrological Model: Impact of Observation Length and Information. Journal of Hydrology 643.PNNL-SA-194809.doi:10.1016/j.jhydrol.2024.131889

Deines J.M., S. Archontoulis, I. Huber, and D.B. Lobell. 2024. Observational evidence for groundwater influence on crop yields in the United States. Proceedings of the National Academy of Sciences (PNAS) 121, no. 36:Art. No. e2400085121.PNNL-SA-195476.doi:10.1073/pnas.2400085121

Hysmith H., E. Foadian, S.P. Padhy, S.V. Kalinin, R.G. Moore, O.S. Ovchinnikova, and M. Ahmadi. 2024. [JOINT APPOINTEE]: The future of self-driving laboratories: From Human in the Loop Interactive AI to Gamification. Digital Discovery 3, no. 4:621-636.PNNL-SA-193852.doi:10.1039/D4DD00040D

Overstreet R.E., E. King, G.P. Clopton, J.H. Nguyen, and D.K. Ciesielski. 2024. QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction. Journal of Chemical Information and Modeling 64, no. 15:5806–5816.PNNL-SA-195950.doi:10.1021/acs.jcim.4c00446

Royse E., A. Manzanares, H. Wang, K. Haudek, C. Azzarello, L. Horne, and D. Druckenbrod, et al. 2024. FEW Questions, Many Answers: using machine learning to assess how students connect Food-Energy-Water (FEW) concepts. Humanities and Social Sciences Communication 11, no. _:Art No. 1033.PNNL-SA-191494.doi:10.1057/s41599-024-03499-z

Du J., V. Ishwar Hegde, J.E. Saal, B.J. Riley, and J.D. Vienna. 2024. AI/ML-assisted Design of Phosphate Glass and Ceramic Nuclear Waste Forms. Transactions of the American Nuclear Society 130, no. 1:144-147.PNNL-SA-195362.

Lin X., J. Fan, Y. Zhang, and Z. Hou. 2024. Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms. Advances in Atmospheric Sciences 41, no. 7:1450–1462.PNNL-SA-201265.doi:10.1007/s00376-024-3198-7

Valencia Acuna P.A., K. Rijal, C. Wang, M.A. Ziatdinov, W. Chan, and P.Z. El-Khoury. 2024. Visualizing nanoscale heterogeneity in perylene thin films via tip-enhanced photoluminescence with unsupervised machine learning. Chemical Communications 60, no. 58:7435-7438.PNNL-SA-193548.doi:10.1039/D4CC01808G

Suematsu T., Z. Martin, E. Barnes, C.A. Demott, S.M. Hagos, Y. Ham, and D. Kim, et al. 2024. Incorrect computation of Madden-Julian oscillation prediction skill. npj Climate and Atmospheric Science 7, no. _:Art. No. 134.PNNL-SA-200874.doi:10.1038/s41612-024-00687-1

Sun Y., H.A. Pahlavan, A. Chattopadhyay, P. Hassanzadeh, S. Lubis, M. Alexander, and E. Gerber, et al. 2024. Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM. Journal of Advances in Modeling Earth Systems 16, no. 7:Art. No. e2023MS004145.PNNL-SA-195382.doi:10.1029/2023MS004145

Mamud M., M. Mudunuru, S. Karra, and B. Ahmmed. 2024. Quantifying local and global mass balance errors in physics-informed neural networks. Scientific Reports 14, no. _:Art. No. 15541.PNNL-SA-198826.doi:10.1038/s41598-024-65472-9

Lu X., Z.D. Weller, V. Gervasio, and J.D. Vienna. 2024. Glass Design Using Machine Learning Property Models with Prediction Uncertainties: Nuclear Waste Glass Formulation. Journal of Non-crystalline Solids 631.PNNL-SA-193529.doi:10.1016/j.jnoncrysol.2024.122907

Chen C., D. Nguyen, S.J. Lee, N.A. Baker, A.S. Karakoti, L. Lauw, and C. Owen, et al. 2024. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation. Journal of the American Chemical Society 146, no. 29:Art. No. 20009.PNNL-ACT-SA-10823.doi:10.1021/jacs.4c03849

Kim Y., A. Kahana, R. Yin, Y. Li, P. Stinis, G.E. Karniadakis, and P. Panda. 2024. Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding. Frontiers in Neuroscience 18, no. _:Art. No. 1346805.PNNL-SA-193461.doi:10.3389/fnins.2024.1346805

King F., C. Pettersen, C.G. Fletcher, and A.V. Geiss. 2024. Development of a full-scale connected U-Net for reflectivity inpainting in spaceborne radar blind zones. Artificial Intelligence for the Earth Systems 3, no. 2:e230063.PNNL-SA-190355.doi:10.1175/AIES-D-23-0063.1

Shaw T.A., P. Arias, M. Collins, D. Coumou, A. Diedhiou, C.I. Garfinkle, and S. Jain, et al. 2024. Regional Climate Change: Consensus, Discrepancies, and Ways Forward. Frontiers in Climate 6.PNNL-SA-198550.doi:10.3389/fclim.2024.1391634

Feron S., A. Malhotra, S. Bansal, E. Fluet-Chouinard, G. Mcnicol, S.H. Knox, and K.B. Delwiche, et al. 2024. Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems. Global Change Biology 30, no. 1:Art. No. e17131.PNNL-SA-190015.doi:10.1111/gcb.17131

Bandai T., T.A. Ghezzehei, P. Jiang, P. Kidger, X. Chen, and C.I. Steefel. 2024. Learning constitutive relations from soil moisture data via physically constrained neural networks. Water Resources Research 60, no. 7:Art. No. e2024WR037318.PNNL-SA-194980.doi:10.1029/2024WR037318

Munikoti S., B. Natarajan, and M. Halappanavar. 2024. GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization. Journal of Parallel and Distributed Computing 192.PNNL-SA-198119.doi:10.1016/j.jpdc.2024.104900

Bennett A., H.V. Tran, L. De La Fuente, A. Triplett, Y. Ma, P. Melchior, and R.M. Maxwell, et al. 2024. Spatio-temporal machine learning for regional to continental scale terrestrial hydrology. Journal of Advances in Modeling Earth Systems 16, no. 6:e2023MS004095.PNNL-SA-198599.doi:10.1029/2023MS004095

L'Abbate R., A. D'Onofrio, S.A. Stein, S. Chen, A. Li, P. Chen, and J. Chen, et al. 2024. A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity. IEEE Transactions on Quantum Engineering 5.PNNL-SA-195015.doi:10.1109/TQE.2024.3367234

Fu Y., A.A. Howard, C. Zeng, Y. Chen, P. Gao, and P. Stinis. 2024. Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance. ACS Energy Letters 9, no. 6:2767-2774.PNNL-SA-192565.doi:10.1021/acsenergylett.4c00493

Howard A.A., Y. Fu, and P. Stinis. 2024. A multifidelity approach to continual learning for physical systems. Machine Learning: Science and Technology 5, no. 2:Art No. 25042.PNNL-SA-198022.doi:10.1088/2632-2153/ad45b2

Hossain R.R., T. Yin, Y. Du, R. Huang, J. Tan, W. Yu, and Y. Liu, et al. 2024. Efficient Learning of Power Grid Voltage Control Strategies via Model-based Deep Reinforcement Learning. Machine Learning 113.PNNL-SA-172082.doi:10.1007/s10994-023-06422-w

Song Y., X. Lu, K. Wang, J.V. Ryan, M.M. Smedskjaer, J.D. Vienna, and M. Bauchy. 2024. Unveiling the Effect of Composition on Nuclear Waste Immobilization Glasses’ Durability by Nonparametric Machine Learning. npj Materials Degradation 8.PNNL-SA-190927.doi:10.1038/s41529-024-00458-6

Li L., G. Bisht, D. Hao, and L. Leung. 2024. Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling. Earth System Science Data 16, no. 4:2007–2032.PNNL-SA-186655.doi:10.5194/essd-16-2007-2024

Sizemore L., B.J. Hutchinson, and E. Borda. 2024. Use of Machine Learning to Analyze Chemistry Card Sort Tasks. Chemistry Education Research & Practice 25, no. 2:417-437.PNNL-SA-195093.doi:10.1039/D2RP00029F

Zhang Y., V. Bharathi, T. Dokoshi, J. de Anda, L. Tumey Ursery, N.N. Kulkarni, and Y. Nakamura, et al. 2024. Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes. Proceedings of the National Academy of Sciences (PNAS) 121, no. 6:Art. No. e2300644120.PNNL-SA-192774.doi:10.1073/pnas.2300644120

Kim D., A.D. McNaughton, and N. Kumar. 2024. Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions. Bioengineering (Basel) 11, no. 2:Art. No. 185.PNNL-SA-194122.doi:10.3390/bioengineering11020185

Oostrom M.T., M. Muniak, R.M. Eichler West, S.M. Akers, P. Pande, M.Y. Obiri, and W. Wang, et al. 2024. Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images. PLoS One 19, no. 3:Art. No. e0293856.PNNL-SA-190650.doi:10.1371/journal.pone.0293856

Chen W., and P. Stinis. 2024. Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. Journal of Computational Physics 498.PNNL-SA-182880.doi:10.1016/j.jcp.2023.112683

Roy A., A.R. Swope, R. Devanathan, and I. van Rooyen. 2024. Chemical composition based machine learning model to predict defect formation in additive manufacturing. Materialia 33.PNNL-SA-189350.doi:10.1016/j.mtla.2024.102041

Helal H., J.S. Firoz, J.A. Bilbrey, H.W. Sprueill, K.M. Herman, M.M. Krell, and T. Murray, et al. 2024. Acceleration of Graph Neural Network-based Prediction Models in Chemistry via Co-design Optimization on Intelligence Processing Units. Journal of Chemical Information and Modeling 64, no. 5:1568–1580.PNNL-SA-193670.doi:10.1021/acs.jcim.3c01312

Yu S., P. Ma, B. Singh, S. Silva, and M.S. Pritchard. 2024. TWO-STEP HYPERPARAMETER OPTIMIZATION METHOD: ACCELERATING HYPERPARAMETER SEARCH BY USING A FRACTION OF A TRAINING DATASET. Artificial Intelligence for the Earth Systems 3, no. 1:Art. No. e230013.PNNL-SA-191873.doi:10.1175/AIES-D-23-0013.1

Yue C., J. Jian, P. Ciais, X. Ren, J. Jiao, and B. Bond-Lamberty. 2024. Field experiments show no consistent reductions in soil microbial carbon in response to warming. Nature Communications 15.PNNL-SA-181067.doi:10.1038/s41467-024-45508-4

Bond-Lamberty B., A. Ballantyne, E. Berryman, E. Fluet-Chouinard, J. Jian, K.A. Morris, and A. Rey, et al. 2024. Twenty years of progress, challenges, and opportunities in measuring and understanding soil respiration. Journal of Geophysical Research: Biogeosciences 129, no. 2:e2023JG007637.PNNL-SA-192425.doi:10.1029/2023JG007637

Yang Z., A.D. GAIDHANE, J. Drgona, V. Chandan, M. Halappanavar, F. Liu, and Y. Cao. 2024. Physics-constrained graph modeling for building thermal dynamics. Energy and AI 16.PNNL-SA-195071.doi:10.1016/j.egyai.2024.100346

Narayanan A., K. Balaguru, W. Xu, and L. Leung. 2024. A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters. Natural Hazards 120, no. 1:881–899.PNNL-SA-180864.doi:10.1007/s11069-023-06100-z

Hou Z., N.D. Ward, A.N. Myers-Pigg, X. Lin, S.R. Waichler, C.A. Wiese Moore, and M.J. Norwood, et al. 2024. Quantifying Drivers of Methane Hydrobiogeochemistry in a Tidal River Floodplain System. Water 16, no. 1:Art. No. 171.PNNL-SA-158802.doi:10.3390/w16010171

Qin C., K. Guddanti, B. Vyakaranam, T.B. Nguyen, K. Mahapatra, Q.H. Nguyen, and Z. Hou, et al. 2024. Critical Zone Identification Framework for Bulk Electric System Security Assessment. International Journal of Electrical Power & Energy Systems 115, no. Part B:Art. No. 109542.PNNL-SA-183659.doi:10.1016/j.ijepes.2023.109542

Chen W., P. Gao, and P. Stinis. 2024. Physics-informed machine learning of the correlation functions in bulk fluids. Physics of Fluids 36, no. 1:Art. No. 017133.PNNL-SA-194310.doi:10.1063/5.0175065

Ni Y., Y. Yang, H. Wang, H. Li, M. Li, P. Wang, and K. Li, et al. 2024. Contrasting changes in ozone during 2019–2021 between eastern and the other regions of China attributed to anthropogenic emissions and meteorological conditions. Science of the Total Environment 908.PNNL-SA-189750.doi:10.1016/j.scitotenv.2023.168272

Zhou Y., P. Jiang, P. Chen, E. Jia, C.S. Welch, Q. Zhao, and J.A. Dhas, et al. 2024. Novel Principal Component Analysis Tool based on Python for Analysis of Complex Spectra of Time-of-Flight Secondary Ion Mass Spectrometry. Journal of Vacuum Science & Technology A: International Journal Devoted to Vacuum, Surfaces, and Films A42, no. 2:Art. No. 023204.PNNL-SA-189615.doi:10.1116/6.0003355

Bramer L.M., H.M. Dixon, D. Rohlman, R.P. Scott, R.L. Miller, L. Kincl, and J.B. Herbstman, et al. 2024. PM2.5 is insufficient to explain personal PAH exposure. Geohealth 8, no. 2:Art. No. e2023GH000937.PNNL-SA-184349.doi:10.1029/2023GH000937

Conference Papers

Wu C., R. Song, C. Liu, Y. Yang, A. Li, M. Huang, and T. Geng. 2024. Extending Power of Nature from Binary Problems to Real-Valued Graph Learning in Real World. Proceedings of the Twelfth International Conference on Learning Representations (ICLR 2024), May 7, 2024 Vienna, Austria 2024, 2334-2339. Appleton, Wisconsin: International Conference on Learning Representations.PNNL-SA-194054.

Zheng M., Y. Chen, X. Yang, and A. Li. 2024. Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems. IEEE Power & Energy Society General Meeting (PESGM 2024), July 21-25, 2024, Seattle, WA, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-191945.doi:10.1109/PESGM51994.2024.10688916

Glass S.W., J.R. Tedeschi, M.P. Spencer, J. Son, M. Taufique, D. Li, and M. Elen, et al. 2024. Spread spectrum time domain reflectometry (SSTDR) and frequency domain reflectometry (FDR) cable inspection using machine learning. Proceedings of the ASME 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2024), QNDE2024-133645, V001T11A001. New York, New York: American Society of Mechanical Engineers.PNNL-SA-195124.doi:10.1115/QNDE2024-133645

Myers A.D., T.J. Doster, C.C. Olson, and T.H. Emerson. 2024. Topological and Dynamical Representations for Radio Frequency Signal Classification. ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, July 29, 2024 Vienna, Austria, 1-10. San Diego, California:International Conference on Machine Learning.PNNL-SA-199402.

Cali U., S. Gourisetti, D.J. Sebastian Cardenas, F.O. Catak, A. Lee, L. Zeger, and T.S. Ustun, et al. 2024. Emerging Technologies for Privacy Preservation in Energy Systems. Proceedings of the 2024 European Interdisciplinary Cybersecurity Conference (EICC 2024), June 5-6, 2024, Xanthi, Greece, edited by S. Li and K. Coopamootoo, 163 - 170. New York, New York: Association for Computing Machinery.PNNL-SA-196446.doi:10.1145/3655693.3656546

Haghi P., C. Tan, A. Guo, C. Wu, D. Liu, A. Li, and A. Skjellum, et al. 2024. SmartFuse: Reconfigurable Smart Switches to Accelerate Fused Collectives in HPC Applications. Proceedings of the 38th ACM International Conference on Supercomputing (ICS 2024), June 4-7, 2024, Kyoto, Japan, 413–425. New York, New York: Association for Computing Machinery.PNNL-SA-197072.doi:10.1145/3650200.3656616

Howland S., K.S. Kappagantula, H.J. Kvinge, and T.H. Emerson. 2024. Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss. Proceedings of the ICML 2024 Workshop on Geometry-grounded Representation Learning and Generative Modeling, July 29, 2024 Vienna, Austria, 1-10. San Diego, California: International Conference on Machine Learning.PNNL-SA-200750.

Das S., S.M. Ferdous, M. Halappanavar, E. Serra, and A. Pothen. 2024. AGS-GNN: Attribute-guided Sampling for Graph Neural Networks. KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 25-29, 2024, Barcelona, Spain, 538 - 549. New York, New York:Association for Computing Machinery.PNNL-SA-198816.doi:10.1145/3637528.3671940

Wang Z., Y. Wang, J. Deng, D. Zheng, A. Li, and Y. Ding. 2024. RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024), April 27- May 1, 2024, San Diego, CA, 2, 964-979. New York, New York:Association for Computing Machinery.PNNL-SA-189479.doi:10.1145/3620665.3640406

Hannan D.W., R.I. Arnab, G.G. Parpart, G.T. Kenyon, E. Kim, and Y.Z. Watkins. 2024. Event-to-Video Conversion for Overhead Object Detection. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI 2024), 89-92. Los Alamitos, California:IEEE Computer Society.PNNL-SA-191687.doi:10.1109/SSIAI59505.2024.10508655

Pritchard S., B. Mitra, and V. Nagaraju. 2024. Three-Stage Adjusted Regression Forecasting for Software Defect Prediction. Annual Reliability and Maintainability Symposium (RAMS 2024), January 22-25, 2024, Albuquerque, NM, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-188596.doi:10.1109/RAMS51492.2024.10457812

Saranathan G., M. Foltin, A. Tripathy, A. Justine, M.A. Ziatdinov, A. Ghosh, and K. Roccapriore, et al. 2024. Towards FAIR Workflows for Federated Experimental Sciences. IEEE Conference on Artificial Intelligence (CAI 2024), June 25-27, 2024, Singapore, 1436-1437. Los Alamitos, California:IEEE Computer Society.PNNL-SA-202274.doi:10.1109/CAI59869.2024.00256

Hagen A.R., S.W. Jackson, J.L. Yaros, N.H. Ly, and C.A. Nizinski. 2024. A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models. Proceedings of the 65th Annual Meeting of the Institute of Nuclear Materials Management, July 21-25, 2024, Portland, OR, 1-12. Mount Laurel, New Jersey:Institute of Nuclear Materials Management.PNNL-SA-200177.

Byler E.B., B.M. Forland, and M.A. McKay. 2024. Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques. SPIE Defense and Commercial Sensing Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, April 21-26, 2024, National Harbor, MD. Proceedings of the SPIE, edited by M. Velez-Reyes and D.W. Messinger, 13031, Paper No. 130310I. Bellingham, Washington:SPIE (International Society for Optical Engineering).PNNL-SA-196903.doi:10.1117/12.3012800

Yin T., S. Wulff, J.W. Pierre, and B. Amidan. 2024. Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System. International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA 2024), May 21-23, 2024, Washington, D.C., 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-189991.doi:10.1109/SGSMA58694.2024.10571471

Limaye A.M., C. Barone, N. Bohm Agostini, M. Minutoli, J.B. Manzano Franco, V.G. Castellana, and G. Gozzi, et al. 2024. Towards Automated Generation of Chiplet-Based Systems. Proceedings of the 29th Asia and South Pacific Design Automation Conference (ASPDAC 2024), January 22-25, 2024, Incheon, South Korea, 771-776. Piscataway, New Jersey:IEEE.PNNL-SA-192586.doi:10.1109/ASP-DAC58780.2024.10473980

Yarlott W.H., A. Acharya, D. Castro Estrada, D. Gomez, and M.A. Finlayson. 2024. GOLEM: GOld standard for Learning and Evaluation of Motifs. Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 20-24, 2024, Torino, Italy, edited by N. Calzolari, et al, 7801–7813. Kerrville, Texas:Association for Computational Linguistics.PNNL-SA-191514.

Wenskovitch J.E., C.K. Fallon, K. Miller, and A. Dasgupta. 2024. Characterizing Interaction Uncertainty in Human-Machine Teams. IEEE 4th International Conference on Human-Machine Systems (ICHMS 2024), May 15-17, 2024 Toronto, ON, Canada, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-192637.doi:10.1109/ICHMS59971.2024.10555605

Islam M., S.B. Dutta, A. Marquez, I. Alouani, and K.N. KHASAWNEH. 2024. Harnessing ML Privacy by Design Through Crossbar Array Non-idealities. Design, Automation and Test in Europe Conference (DATE 2024), March 25-27, 2024, Valencia, Spain, 1-2. Piscataway, New Jersey:IEEE.PNNL-SA-194845.

Wang M., B. Fang, A. Li, and P. Nair. 2024. Red-QAOA: Efficient Variational Optimization through Circuit Reduction. ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024), April 27-May 1, 2024, La Jolla, CA, 2, 990-998. New York, New York:Association for Computing Machinery.PNNL-SA-192496.doi:10.1145/3620665.3640363

Ramesh M., K. Chatterjee, D.M. Glover, J.D. Follum, T.E. Mcdermott, and A.P. Reiman. 2024. Convolutional Neural Network-Based Protection-Zone Classification of Faults in Distribution Feeders with Photovoltaics. IEEE Green Technologies Conference (GreenTech 2024), April 3-5, 2024, Springdale, AR, 173-177. Piscataway, New Jersey:IEEE.PNNL-SA-194357.doi:10.1109/GreenTech58819.2024.10520556

Neff R.W., M.E. Zarch, M. Minutoli, M. Halappanavar, A. Tumeo, A. Kalyanaraman, and M. Becchi. 2024. FuseIM: Fusing Probabilistic Traversals for Influence Maximization on Exascale Systems. Proceedings of the 38th ACM International Conference on Supercomputing (ICS 2024), June 4-7, 2024, Kyoto, Japan, 38-49. New York, New York:Association for Computing Machinery.PNNL-SA-188692.doi:10.1145/3650200.3656621

Peng H., C. Ding, T. Geng, S. Choudhury, K.J. Barker, and A. Li. 2024. Evaluating Emerging AI/ML Accelerators: IPU, RDU, and and NVIDIA/AMD GPUs. Companion of the 15th ACM/SPEC International Conference on Performance Engineering (ICPE 2024), May 7-11, 2024, London, 14 - 20. New York, New York:Association for Computing Machinery.PNNL-SA-188060.doi:10.1145/3629527.3651428

Song R., C. Wu, C. Liu, A. Li, M. Huang, and T. Geng. 2024. DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems. ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA 2024), June 29-July 3, 2024, Buenos Aires, Argentina, 45-57. Los Alamitos, California:IEEE Computer Society.PNNL-SA-196761.doi:10.1109/ISCA59077.2024.00014

Ferdous S.M., A. Pothen, and M. Halappanavar. 2024. Streaming Matching and Edge Cover in Practice. Proceedings of the 22nd International Symposium on Experimental Algorithms (SEA 2024), July 24-26, Vienna, Austria. Leibniz International Proceedings in Informatics (LIPIcs), 301, 12:1-12:22.PNNL-SA-197168.doi:10.4230/LIPIcs.SEA.2024.12

Ferdous S.M., R.W. Neff, B. Peng, S.S. Shuvo, M. Minutoli, S. Mukherjee, and K. Kowalski, et al. 2024. Picasso: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing. IEEE International Parallel and Distributed Processing Symposium (IPDPS 2024), May 27-31. 2024, San Francisco, CA, 241-252. Piscataway, New Jersey:IEEE.PNNL-SA-191110.doi:10.1109/IPDPS57955.2024.00029

Bohm Agostini N., P. Gibson, J. Haris, M. Jayaweera, N. Rubin, A. Tumeo, and J.L. Abellán, et al. 2024. AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators. IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2024), March 2-6, 2024, Edinburgh, UK, 143-157. Piscataway, New Jersey:IEEE.PNNL-SA-184683.doi:10.1109/CGO57630.2024.10444801

Li J., A. Li, and W. Jiang. 2024. Quapprox: A Framework for Benchmarking the Approximability of Variational Quantum Circuit. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024), April 14-19, 2024 Seoul, Republic of Korea, 13376-13380. Piscataway, New Jersey:IEEE.PNNL-SA-193669. doi:10.1109/ICASSP48485.2024.10447919

Bramer L.M., H.M. Dixon, D.J. Degnan, D. Rohlman, J.B. Herbstman, K.A. Anderson, and K.M. Waters. 2024. Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (Biocomputing 2024), January 3-7, 2024, Kohala Coast, HI, edited by R.B. Altman, L. Hunter, M.D. Ritchie, and T. Murray, 170-186.PNNL-SA-188524. doi:10.1142/9789811286421_0014

Mathieu R., S. Boamah, A. Cooper, D. Agnew Jr., J. Mcnair, and A. Bretas. 2024. Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid. 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1-5. Washington, Dc, District Of Columbia:IEEE.PNNL-SA-190163. doi:10.1109/ISGT59692.2024.10454165

Reports

Tu J.H., R.M. Eichler West, E.J. Ellwein, and J.M. Vann. 2024. Augmented Human Analysis (AHA). Richland, WA: Pacific Northwest National Laboratory.

Engel A.W., and J.F. Strube. 2024. Open Call LDRD: Physically Informed Autoencoders for Galactic Redshift Regression. Richland, WA: Pacific Northwest National Laboratory.

Eichler West R.M., J.H. Tu, A.T. Rupe, T. Chen, and I.P. Finley. 2024. Predicting Critical Transitions in Multiscale Data. Richland, WA: Pacific Northwest National Laboratory.

Chen Y., C. Zeng, Y. Fu, J. Bao, P. Gao, J.Q. Chen, and Z. Xu, et al. 2024. Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework. Richland, WA: Pacific Northwest National Laboratory.

Liu Y., H. Huang, T. Zhang, D. Xu, and Y. Chen. 2024. Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model. Richland, WA: Pacific Northwest National Laboratory.

Stinis P., and S. Qadeer. 2024. Machine-learning based model reduction for partial differential equations. Richland, WA: Pacific Northwest National Laboratory.

Chau H.H., H.K. Jenne, D.R. Brown, S. Billey, M.V. Raugas, and H.J. Kvinge. 2024. Machine Learning meets Algebraic Combinatorics: A Suite of Benchmark Datasets to Accelerate AI for Mathematics Research. Richland, WA: Pacific Northwest National Laboratory.

Aksoy S.G., B. Fang, R. Gioiosa, W.W. Kay, H. Lee, J.A. Bilbrey, and M.R. Shapiro, et al. 2024. Unifying Combinatorial and Graphical Methods in Artificial Intelligence. Richland, WA: Pacific Northwest National Laboratory.

Varikoti R.A., C. Kombala Nanayakkara Thambiliya, S.M. Thibert, D.J. Reid, M. Zhou, A. Kruel, and N. Kumar. 2024. Automated AI-driven Molecular Design for Therapeutic Discovery. Richland, WA: Pacific Northwest National Laboratory.

Ferracina F., P.A. Beeler, M. Halappanavar, B. Krishnamoorthy, M. Minutoli, and L.M. Fierce. 2024. Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators. Richland, WA: Pacific Northwest National Laboratory.

Marquez A., T.C. Fujimoto, and T.J. Stavenger. 2024. Assurance of Reasoning Enabled Systems (ARES). Richland, WA: Pacific Northwest National Laboratory.

Eloe-Fadrosh E., P. Chain, S. Cholia, K. Fagnan, D.M. Mans, L. McCue, and C.J. Mungall, et al. 2024. DOE BSSD Performance Management Metrics Report Q3. Richland, WA: Pacific Northwest National Laboratory.

Chen Y., X. Fan, R. Huang, Q. Huang, A. Li, and K. Guddanti. 2024. Artificial Intelligence/Machine Learning Technology in Power System Applications. Richland, WA: Pacific Northwest National Laboratory.

Kim J. 2024. Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets - CRADA 422 (Abstract) Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Stegmann A., B.A. Legg, J.J. De Yoreo, and S. Zhang. 2024. Machine learning-driven descriptions of protein dynamics at solid-liquid interfaces. Machine learning and artificial intelligence in chemical and biological sensing, edited by J.Y. Yoon and C. Yu. 321-340. Philadelphia, Pennsylvania:Elsevier Science.PNNL-SA-194353.doi:10.1016/B978-0-443-22001-2.00013-5

2023

Journal articles

Ferrocino I., K. Rantsiou, R.S. McClure, T. Kostic, R. Soares Correa De Souza, L. Lange, and J. Fitzgerald, et al. 2023. The need for an integrated multi-OMICs approach in microbiome science in the food system. Comprehensive Reviews in Food Science and Food Safety 22, no. 2:1082-1103.PNNL-SA-176734.doi:10.1111/1541-4337.13103

Terry N.C., F.D. Day-Lewis, J.W. Lane, C.D. Johnson, C.D. Johnson, and D. Werkema. 2023. Field evaluation of semi-automated moisture estimation from geophysics using machine learning. Vadose Zone Journal 22, no. 2:Art. No. e20246.PNNL-SA-172904.doi:10.1002/vzj2.20246

Scott E., M. Coletti, C. Schuman, W.W. Kay, S. Kulkarni, M. Parsa, and C. Gunaratne, et al. 2023. Avoiding Excess Computation in Asynchronous Evolutionary Algoriths. Expert Systems 40, no. 5:Art. No. e13100.PNNL-SA-171021.doi:10.1111/exsy.13100

Chen X., L. Leung, and N. Sun. 2023. Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes. Geophysical Research Letters 50, no. 24:Art. No. e2023GL105530.PNNL-SA-187578.doi:10.1029/2023GL105530

Wang X., A. Hsu, and T. Chakraborty. 2023. Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress. Environmental Research: Infrastructure and Sustainability 3, no. 3:Art. No. 035003.PNNL-SA-189592. doi:10.1088/2634-4505/acef57

Bylinkin A., C. Dean, S. Fegan, D. Gangadharan, K. Gates, S. Kay, and I. Korover, et al. 2023. Detector requirements and simulation results for the EIC exclusive, diffractive and tagging physics program using the ECCE detector concept. Nuclear Instruments and Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1052.PNNL-SA-194382. doi:10.1016/j.nima.2023.168238

Wu B., H. Ouro-Koura, S. Lu, H. Li, X. Wang, J. Xiao, and Z. Deng. 2023. Functional Materials for Powering and Implementing Next-Generation Miniature Sensors. Materials Today 69.PNNL-SA-186034.doi:10.1016/j.mattod.2023.09.001

Sarkar D., H. Lee, J.W. Vant, M. Turilli, J.V. Vermaas, S. Jha, and A. Singharoy. 2023. Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting. Journal of Chemical Information and Modeling 63, no. 18:5834–5846.PNNL-SA-189482.doi:10.1021/acs.jcim.3c00350

Tran V.N., V. Ivanov, D. Xu, and J. Kim. 2023. Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models. Geophysical Research Letters 50, no. 17:Art. No. e2023GL104464.PNNL-SA-185856.doi:10.1029/2023GL104464

Nguyen M., V. Glezakou, R. Rousseau, and P.D. Paviet. 2023. Exploring NaCl-PuCl3 Molten Salts with Machine Learning Interatomic Potentials and Graph Theory. Applied Materials Today 35.PNNL-SA-185810.doi:10.1016/j.apmt.2023.101951

Sweeney A.J., Q. Fu, S. Po-Chedley, H. Wang, and M. Wang. 2023. Internal Variability Increased Arctic Amplification During 1980-2022. Geophysical Research Letters 50, no. 24:e2023GL106060.PNNL-SA-186763.doi:10.1029/2023GL106060

Karra S., M. Mehana, N. Lubbers, Y. Chen, A. Diaw, J. Santos, and A. Pachalieva, et al. 2023. Predictive Scale-Bridging Simulations through Active Learning. Scientific Reports 13.PNNL-SA-174843.doi:10.1038/s41598-023-42823-6

Wang D., J. Bao, M. Zamarripa-Perez, B. Paul, Y. Chen, P. Gao, and T. Ma, et al. 2023. A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems. Energy Advances 2, no. 10:1735-1751.PNNL-SA-178647.doi:10.1039/d3ya00310h

Platt J., S. Penny, T. Smith, T. Chen, and H. Abarbanel. 2023. Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers. Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 10:Art. No. 103107.PNNL-SA-191042.doi:10.1063/5.0156999

Keshava Murthy R., and L.E. Charles. 2023. Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning. Scientific Reports 13.PNNL-SA-192685.doi:10.1038/s41598-023-38074-0

Fan R., R. Huang, S. Wang, and J. Zhao. 2023. Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration. IEEE Transactions on Power Systems 38, no. 4:3787 - 3798.PNNL-SA-192633.doi:10.1109/TPWRS.2022.3208021

Shi H., Y. Dazhi, W. Wenting, D. Fu, J. Zhang, G. Ling, and B. Hu, et al. 2023. First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning. Renewable & Sustainable Energy Reviews 184.PNNL-SA-192846.doi:10.1016/j.rser.2023.113549

Yaman M.Y., S.V. Kalinin, K.N. Guye, D.S. Ginger, and M. Ziatdinov. 2023. Learning and predicting photonic responses of plasmonic nanoparticle assemblies via dual variational autoencoders. Small 19, no. 25:Art. No. 2205893.PNNL-SA-180000.doi:10.1002/smll.202205893

Nobre G., D. Brown, S.J. Hollick, S. Scoville, and P.J. Rodriguez Fernandez. 2023. Novel machine-learning method for spin classification of neutron resonances. Physical Review C 107, no. 3:Art. No. 034612.PNNL-SA-181610.doi:10.1103/PhysRevC.107.034612

Branch S.D., H.M. Felmy, A. Schafer Medina, S.A. Bryan, and A.M. Lines. 2023. Exploring the complex chemistry of Uranium within molten chloride salts. Industrial and Engineering Chemistry Research 62, no. 37:14901–14909.PNNL-SA-181742.doi:10.1021/acs.iecr.3c02005

Howard A.A., J. Dong, R. Patel, M. D'Elia, M. Maxey, and P. Stinis. 2023. Machine learning methods for particle stress development in suspension Poiseuille flows. Rheologica Acta 62, no. 10:507–534.PNNL-SA-182934.doi:10.1007/s00397-023-01413-z

Kalinin S.V., D. Mukherjee, K. Roccapriore, B. Blaiszik, A. Ghosh, M. Ziatdinov, and A. Al-Najjar, et al. 2023. Machine Learning for Automated Experimentation in Scanning Transmission Electron Microscopy. npj Computational Materials 9.PNNL-SA-183322.doi:10.1038/s41524-023-01142-0

Sun H., B. Bond-Lamberty, T. Hu, J. Li, J. Jian, Z. Xu, and B. Jia. 2023. Forest soil carbon efflux evaluation across China: a new estimate with machine learning. Global Biogeochemical Cycles 37, no. 8:Art. No. e2023GB007761.PNNL-SA-162066.doi:10.1029/2023GB007761

Regier P.J., N.D. Ward, A.N. Myers-Pigg, J.W. Grate, M. Freeman, and R.N. Ghosh. 2023. Seasonal drivers of dissolved oxygen across a tidal creek–marsh interface revealed by machine learning. Limnology and Oceanography 68, no. 10:2359-2374.PNNL-SA-181936.doi:10.1002/lno.12426

Smith T., S. Penny, J. Platt, and T. Chen. 2023. Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence. Journal of Advances in Modeling Earth Systems 15, no. 12:Art. No. e2023MS003792.PNNL-SA-183691.doi:10.1029/2023MS003792

Stromberg Z.R., S. Phillips, K.M. Omberg, and B.M. Hess. 2023. High-throughput functional trait testing for bacterial pathogens. mSphere 8, no. 5:e00315-23.PNNL-SA-179628. doi:10.1128/msphere.00315-23

Narayanan A., and T.D. Hardy. 2023. Synthetic data generation for machine learning model training for energy theft scenarios using cosimulation. IET Generation, Transmission and Distribution 17, no. 5:1035-1046.PNNL-SA-168747. doi:10.1049/gtd2.12619

Lyons S.M., C.G. Britt, L.S. Wood, D.L. Duke, B.G. Fulsom, M.E. Moore, and L. Snyder. 2023. Machine Learning Methods for Fission Product Identification from Bragg Curves. AIP Advances 13, no. 8:Art. No. 085115.PNNL-SA-178019. doi:10.1063/5.0142716

Niu J., W. Xu, H. Qiu, S. Li, and F. Dong. 2023. 1-D coupled surface flow and transport equations revisited via the physics-informed neural network approach. Journal of Hydrology 625, no. Part B:Art. No. 130048.PNNL-SA-190227. doi:10.1016/j.jhydrol.2023.130048

Liu J., Z. Zhang, X. Li, M. Zong, Y. Wang, S. Wang, and P. Chen, et al. 2023. Machine Learning Assisted Phase and Size-Controlled Synthesis of Iron Oxide Particles. Chemical Engineering Journal 473.PNNL-SA-188720. doi:10.1016/j.cej.2023.145216

Anderson A.A., B.A. Jefferson, S. Kincic, J.E. Wenskovitch, C. Fallon, J.A. Baweja, and Y. Chen. 2023. Human-Centric Contingency Analysis Metrics for Evaluating Operator Performance and Trust. IEEE Access 11.PNNL-SA-177705. doi:10.1109/ACCESS.2023.3322133

Zhao X., Y. Luo, J. Liu, W. Liu, K.M. Rosso, X. Guo, and T. Geng, et al. 2023. Machine Learning Automated Analysis of Enormous Synchrotron X-Ray Diffraction Datasets. Journal of Physical Chemistry C 127, no. 30:14830–14838.PNNL-SA-183442. doi:10.1021/acs.jpcc.3c03572

Howard A.A., M. Perego, G.E. Karniadakis, G.E. Karniadakis, and P. Stinis. 2023. Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems. Journal of Computational Physics 493.PNNL-SA-172145. doi:10.1016/j.jcp.2023.112462

Eswaran S., M.V. Olarte, R.J. Rallo Moya, L.N. Marrlett, J.A. Harper, M.S. Anderson, and E.M. Shapiro, et al. 2023. HT Model: Using the Molecular Transformer for predicting hydrotreating reactions. Energy and Fuels 37, no. 19:14922–14935.PNNL-SA-186589. doi:10.1021/acs.energyfuels.3c02224

Chen W., Y. Fu, and P. Stinis. 2023. Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model. Journal of Power Sources 584.PNNL-SA-185779. doi:10.1016/j.jpowsour.2023.233548

Schram M., K. Rajput, K. Somayaji NS, P. Li, J. St. John, and H. Sharma. 2023. Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex. Physical Review Accelerators and Beams 26, no. 4:Art. No. 044602.PNNL-SA-190245. doi:10.1103/PhysRevAccelBeams.26.044602

Thekdi S.A., U. Tatar, J. Santos, and S. Chatterjee. 2023. Disaster Risk and Artificial Intelligence: A Framework to Characterize Conceptual Synergies and Future Opportunities. Risk Analysis 43, no. 8:1641-1656.PNNL-SA-167006. doi:10.1111/risa.14038

Wang J., R. Hong, E.G. Demicco, J. Tan, R. Lazcano, A.L. Moreira, and Y. Li, et al. 2023. Deep learning integrates histopathology and proteogenomics at a pan-cancer level. Cell Reports Medicine 4, no. 9:Art. No. 101173.PNNL-SA-191052. doi:10.1016/j.xcrm.2023.101173

Hagos S.M., Z. Feng, S. Tai, and J. Chen. 2023. Regional variability in the environmental controls of precipitation regimes in the tropics. Journal of Geophysical Research: Atmospheres 128, no. 18:Art. No. e2023JD038927.PNNL-SA-177978. doi:10.1029/2023JD038927

Nakayasu E.S., L.M. Bramer, C.K. Ansong, A.A. Schepmoes, T.L. Fillmore, M.A. Gritsenko, and T.R. Clauss, et al. 2023. Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity. Cell Reports Medicine 4, no. 7:Art. No. 101093.PNNL-SA-137771. doi:10.1016/j.xcrm.2023.101093

Jackson R.C., B.A. Raut, D. Dematties, S. Collis, N. Ferrier, P. Beckman, and R. Sankaran, et al. 2023. ARMing the Edge: Designing Edge Computing-capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing. Artificial Intelligence for the Earth Systems 2, no. 4:Art. No. 220062.PNNL-SA-185989. doi:10.1175/AIES-D-22-0062.1

Cooper A., A. Bretas, and S. Meyn. 2023. Anomaly Detection in Power System State Estimation: Review and New Directions. Energies 16, no. 18:Art. No. 6678.PNNL-SA-189763. doi:10.3390/en16186678

Zhou J., P. Yang, P. Kots, M. Cohen, Y. Chen, C. Quinn, and A. Boscoboinik, et al. 2023. Tuning the reactivity of carbon surfaces with oxygen-containing functional groups. Nature Communications 14.PNNL-SA-186100. doi:10.1038/s41467-023-37962-3

Roy A., A. Hussain, P. Sharma, G. Balasubramanian, M. Taufique, R. Devanathan, and P. Singh, et al. 2023. Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model. Acta Materialia 257.PNNL-SA-184145. doi:10.1016/j.actamat.2023.119177

He Q., M. Perego, A.A. Howard, G.E. Karniadakis, G.E. Karniadakis, and P. Stinis. 2023. A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. Journal of Computational Physics 492.PNNL-SA-181082. doi:10.1016/j.jcp.2023.112428

Ross D.H., A. Bilbao, J. Lee, and X. Zheng. 2023. mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format. Analytical Chemistry 2023, 95, no. 25:9428-9431.PNNL-SA-177972. doi:10.1021/acs.analchem.3c01653

Wang S., L. Leung, and Y. Qian. 2023. Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models. Journal of Geophysical Research: Atmospheres 128, no. 14:Art. No. e2023JD039154.PNNL-SA-187176. doi:10.1029/2023JD039154

Wang S., L. Leung, and Y. Qian. 2023. Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models. Journal of Geophysical Research: Atmospheres 128, no. 14:Art. No. e2023JD039154.PNNL-SA-187176. doi:10.1029/2023JD039154

Wang S., L. Leung, and Y. Qian. 2023. Extension of large fire emissions from summer to autumn and its drivers in the western US. Earth's Future 11, no. 7:Art. No. e2022EF003086.PNNL-SA-187211. doi:10.1029/2022EF003086

Li L., Y. Fang, Z. Zheng, M. Shi, M. Longo, C.D. Koven, and J.A. Holm, et al. 2023. A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (V2.0). Geoscientific Model Development 16, no. 14:4017–4040.PNNL-SA-180863. doi:10.5194/gmd-16-4017-2023

Oster M.R., E. King, C. Bakker, A. Bhattacharya, S. Chatterjee, and F. Pan. 2023. Multi-Level Optimization with the Koopman Operator for Data-Driven, Domain-Aware, and Dynamic System Security. Reliability Engineering & System Safety 237.PNNL-SA-169379. doi:10.1016/j.ress.2023.109323

Du J., and M. Zhang. 2023. Preventative studies should begin now for detecting AI-generated microscopy images. Matter 6, no. 6:1673–1674.PNNL-ACT-SA-10751. doi:10.1016/j.matt.2023.04.009

Ahmed S., and P. Stinis. 2023. A Multifidelity Deep Operator Network Approach to Closure for Multiscale Systems. Computer Methods in Applied Mechanics and Engineering 414.PNNL-SA-182879. doi:10.1016/j.cma.2023.116161

Li D., Q. Chen, J. Chun, K. Fichthorn, J.J. De Yoreo, and H. Zheng. 2023. Nanoparticle Assembly and Oriented Attachment: Correlating Controlling Factors to the Resulting Structures. Chemical Reviews 123, no. 6:3127–3159.PNNL-SA-178286. doi:10.1021/acs.chemrev.2c00700

Nagy Z., G. Henze, S. Dey, J. Arroyo, L. Helsen, X. Zhang, and B. Chen, et al. 2023. Ten questions concerning reinforcement learning for building energy management. Building and Environment 241.PNNL-SA-186171. doi:10.1016/j.buildenv.2023.110435

Jagtap N.V., M. Mudunuru, and K.B. Nakshatrala. 2023. CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems. Applied Mathematical Modelling 122.PNNL-SA-175300. doi:10.1016/j.apm.2023.04.020

Muller S.E., M.P. Prange, Z. Lu, W.S. Rosenthal, and J.A. Bilbrey. 2023. An open database of computed bulk ternary transition metal dichalcogenides. Scientific Data 10.PNNL-SA-177921. doi:10.1038/s41597-023-02103-4

Mehendale S.G., B. Peng, N. Govind, and Y. Alexeev. 2023. Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansatze for Hybrid Variational Quantum Computing. Journal of Physical Chemistry A 127, no. 20:4526–4537.PNNL-SA-181544. doi:10.1021/acs.jpca.3c00550

King E., Y. Li, S. Hu, and E.A. Machorro. 2023. Physics-informed machine-learning model of temperature evolution under solid phase processes. Computational Mechanics 72, no. 1:125–136.PNNL-SA-178141. doi:10.1007/s00466-023-02289-9

Geiss A.V., P. Ma, B. Singh, and J.C. Hardin. 2023. Emulating Aerosol Optics with Randomly Generated Neural Networks. Geoscientific Model Development 16, no. 9:2355–2370.PNNL-SA-173125. doi:10.5194/gmd-16-2355-2023

Bilbao A., N. Munoz Munoz, J. Kim, D.J. Orton, Y. Gao, K. Poorey, and K.R. Pomraning, et al. 2023. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nature Communications 14, no. 2023:2461.PNNL-SA-174727. doi:10.1038/s41467-023-37031-9

Baweja J.A., B.A. Jefferson, and C. Fallon. 2023. Opportunities for Human Factors in Machine Learning. Frontiers in Artificial Intelligence 6.PNNL-SA-180540. doi:10.3389/frai.2023.1130190

Lee J.H., Y. Choe, S. Ardid, R. Abbasi-Asl, M. McCarthy, and B. HU. 2023. Editorial: Functional Microcircuits in the Brain and in Artificial Intelligent Systems. Frontiers in Computational Neuroscience 17.PNNL-SA-184487. doi:10.3389/fncom.2023.1135507

Volkova S., D.L. Arendt, E.G. Saldanha, M.F. Glenski, E.M. Ayton, J.A. Cottam, and S.G. Aksoy, et al. 2023. Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods. Computational & Mathematical Organization Theory 29.PNNL-SA-156946. doi:10.1007/s10588-021-09351-y

Li A., S.A. Stein, S. Krishnamoorthy, and J.A. Ang. 2023. QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation. ACM Transactions on Quantum Computing 4, no. 2:10, pp 1-26.PNNL-SA-162867. doi:10.1145/3550488

Lin X., J. Fan, Z. Hou, and J. Wang. 2023. Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States. Journal of Advances in Modeling Earth Systems 15, no. 3:Art. No. e2022MS003334.PNNL-SA-183247. doi:10.1029/2022MS003334

Sharma H., M.B. Shrivastava, and B. Singh. 2023. Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest. npj Climate and Atmospheric Science 6, no. 2023:2.PNNL-SA-172595. doi:10.1038/s41612-023-00353-y

McNaughton A.D., R. Joshi, C.R. Knutson, F. Anubhav, K.J. Luebke, J.P. Malerich, and P. Madrid, et al. 2023. Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties. Journal of Chemical Information and Modeling 63, no. 5:1401-1648.PNNL-SA-171633. doi:10.1021/acs.jcim.2c01662

Mudunuru M., B. Ahmmed, E. Rau, V.V. Vesselinov, and S. Karra. 2023. Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico. Energies 16, no. 7:Art. No. 3098.PNNL-SA-181184. doi:10.3390/en16073098

Li H., Y. Yang, J. Jin, H. Wang, K. Li, P. Wang, and H. Liao. 2023. Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data. Atmospheric Chemistry and Physics 23, no. 2:1131-1145.PNNL-SA-174320. doi:10.5194/acp-23-1131-2023

Geiss A.V., and J.C. Hardin. 2023. Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets. Artificial Intelligence for the Earth Systems 2, no. 1:e210012.PNNL-SA-157590. doi:10.1175/AIES-D-21-0012.1

Bilbao Pena A., D.H. Ross, J. Lee, M.T. Donor, S.M. Williams, Y. Zhu, and Y.M. Ibrahim, et al. 2023. MZA: a data conversion tool to facilitate software development and artificial intelligence research in multidimensional mass spectrometry. Journal of Proteome Research 22, no. 2:508–513.PNNL-SA-173694. doi:10.1021/acs.jproteome.2c00313

Feng D., Z. Tan, and Q. He. 2023. Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model. Water Resources Research 59, no. 2:Art. No. e2022WR033168.PNNL-SA-174830. doi:10.1029/2022WR033168

Ang J.A., K.J. Barker, D.L. Vrabie, and G. Kestor. 2023. Codesign for Extreme Heterogeneity: Integrating Custom Hardware with Commodity Computing Technology to Support Next-Generation HPC Converged Workloads. IEEE Internet Computing 27, no. 1:7 - 14.PNNL-SA-175408. doi:10.1109/MIC.2022.3217423

Howland S.A., L. Kassab, K.S. Kappagantula, H.J. Kvinge, and T.H. Emerson. 2023. Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing. Integrating Materials and Manufacturing Innovation 12, no. 1:1–10.PNNL-SA-177366. doi:10.1007/s40192-022-00287-y

Yang Z., X. Luo, Y. Shi, T. Zhou, K. Luo, Y. Lai, and P. Yu, et al. 2023. Controls and variability of soil respiration temperature sensitivity across China. Science of the Total Environment 871.PNNL-SA-175500. doi:10.1016/j.scitotenv.2023.161974

Guddanti K., B. Vyakaranam, K. Mahapatra, Z. Hou, P.V. Etingov, N.A. Samaan, and T.B. Nguyen, et al. 2023. Dynamic Security Analysis Framework for Future Large Grids with High Renewable Penetrations. IEEE Access 11.PNNL-SA-174886. doi:10.1109/ACCESS.2023.3238316

McCullough K.E., D.S. King, S. Chheda, M.S. Ferrandon, T. Goetjen, Z.H. Syed, and T.R. Graham, et al. 2023. High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal-Organic Framework-Supported Catalyst Design. ACS Central Science 9, no. 2:266–276.PNNL-SA-180322. doi:10.1021/acscentsci.2c01422

Tagestad J.D., T.M. Saltiel, and A. Coleman. 2023. Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management. Remote Sensing 15, no. 2:Art. No. 342.PNNL-SA-180627. doi:10.3390/rs15020342

Flores J.E., D.M. Claborne, Z.D. Weller, B.M. Webb-Robertson, K.M. Waters, and L.M. Bramer. 2023. Missing Data in Multi-Omics Integration: Recent Advances Through Artificial Intelligence. Frontiers in Artificial Intelligence 6.PNNL-SA-179732. doi:10.3389/frai.2023.1098308

Meuris B., S. Qadeer, and P. Stinis. 2023. Machine-learning-based spectral methods for partial differential equations. Scientific Reports 13, no. 1:Art. No. 1739.PNNL-SA-168281. doi:10.1038/s41598-022-26602-3

Lu X., L. Deng, S. Gin, B. Parruzot, J. Reiser, J.V. Ryan, and J.D. Vienna, et al. 2023. Predicting initial dissolution rates using structural features from molecular dynamics simulations. Journal of the American Ceramic Society 106, no. 2:1025–1036.PNNL-SA-176987. doi:10.1111/jace.18850

Talsma C.J., K. Solander, M. Mudunuru, B.M. Crawford, and M.R. Powell. 2023. Frost Prediction using Machine Learning and Deep Neural Network Models. Frontiers in Artificial Intelligence 5.PNNL-SA-180159. doi:10.3389/frai.2022.963781

Hou H., H. Ren, P.D. Royer, and X. Yu. 2023. A Geospatial Risk Analysis Graphical User Interface for Identifying Hazardous Chemicals Emission Sources. PeerJ 11.PNNL-SA-174211. doi:10.7717/peerj.14664

Garcia Martin H., T. Radivojevic, J.D. Zucker, K. Bouchard, S. Peisert, D. Arnold, and N.J. Hillson, et al. 2023. Perspectives for self-driving labs in synthetic biology. Current Opinion in Biotechnology 79.PNNL-SA-177878. doi:10.1016/j.copbio.2022.102881

Sharma H., L.D. Marinovici, V.A. Adetola, and H.T. Schaef. 2023. Data-Driven Modeling of Power Generation for a Coal Power Plant Under Cycling. Energy and AI 11.PNNL-SA-171444. doi:10.1016/j.egyai.2022.100214

Sun H., P. Ramuhalli, and R.E. Jacob. 2023. Machine Learning for Ultrasonic Nondestructive Examination of Welding Defects: A Systematic Review. Ultrasonics 127.PNNL-SA-169657. doi:10.1016/j.ultras.2022.106854

Conference papers

Nguyen D., V. Srinivasan, M. Halappanavar, and A. Vullikanti. 2023. Faster approximate subgraph counts with privacy. Advances in Neural Information Processing Systems: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), December 10-16, 2023. New Orleans, LA, 36, 1-31. La Jolla, California:Neural Information Processing Systems Foundation.PNNL-SA-203133.

Yu S., W.M. Hannah, L. Peng, J. Lin, M.A. Bhouri, R. Gupta, and B. Lutjens, et al. 2023. ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), December 10-16, 2023, New Orleans, LA. Advances in Neural Information Processing Systems, edited by A. Oh, et al, 36. San Diego, California: Neural Information Processing Systems Foundation.PNNL-SA-201954.

Lieberman K., J. Diffenderfer, C.W. Godfrey, and B. Kailkhura. 2023. Neural Image Compression: Generalization, Robustness, and Spectral Biases. Advances in Neural Information Processing Systems: Thirty-sixth Conference on Neural Information Processing Systems, December 10-16, 2023. New Orleans, LA, edited by A. Oh, et al, 1-41. San Diego, California:Neural Information Processing Systems.PNNL-SA-185500.

Sprueill H.W., C. Edwards, M.V. Olarte, U. Sanyal, H. Ji, and S. Choudhury. 2023. Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design. Findings of the Association for Computational Linguistics (EMNLP2023), December 6-10, 2023, Singapore, edited by H. Bouamor, J. Pino, and K. Bali, 8348–8365.PNNL-SA-188070.doi:10.18653/v1/2023.findings-emnlp.560

Mudunuru M., B. Ahmmed, and L. Frash. 2023. GeoThermalCloud for EGS – An Open-source, User-friendly, Scalable AI Workflow for Modeling Enhanced Geothermal Systems. Transactions - Geothermal Resources Council, October 1-4, 2023, Reno, NV, 47, 2334 - 233. Davis, California:Geothermal Resources Council.PNNL-SA-187504.

Zhao Z., D. Moscovitz, L. Du, and X. Fan. 2023. Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-the-Meter Solar. IEEE Energy Conversion Congress and Exposition (ECCE 2023), October 29-November 2, 2023, Nashville, TN, 1278-1282. Piscataway, New Jersey:IEEE.PNNL-SA-192618.doi:10.1109/ECCE53617.2023.10362108

Brown D.R., C.W. Godfrey, N.C. Konz, J.H. Tu, and H.J. Kvinge. 2023. Understanding the Inner-Workings of Language Models Through Representation Dissimilarity. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, December 6-10, 2023, Singapore, edited by H. Bouamor, J. Pino, and K. Bali, 6543–6558. Kerrville, Texas:Association for Computational Linguistics.PNNL-SA-186399.doi:10.18653/v1/2023.emnlp-main.403

Tan C., D. Patil, A. Tumeo, G. Weisz, S. Reinhardt, and J. Zhang. 2023. VecPAC: A Vectorizable and Precision-Aware CGRA. IEEE/ACM International Conference on Computer Aided Design (ICCAD 2023), October 28-November 2, 2023, San Francisco, CA, 1-9. Piscataway, New Jersey:IEEE.PNNL-SA-180016.doi:10.1109/ICCAD57390.2023.10323910

Tran H., H. Nguyen, T. Vu, and S.T. Ojetola. 2023. Applying Quantum Computing to Simulate Power System Dynamics. North American Power Symposium (NAPS 2023), October 15-17, 2023, Asheville, NC, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-188910.doi:10.1109/NAPS58826.2023.10318578

Senapati P., Z. Wang, W. Jiang, T.S. Humble, B. Fang, S. Xu, and Q. Guan. 2023. Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices. IEEE International Conference on Quantum Computing and Engineering (QCE 2023), September 17-22, 2023, Bellevue, WA, 468-474. Piscataway, New Jersey:IEEE.PNNL-SA-185102.doi:10.1109/QCE57702.2023.00060

Li J., Z. Wang, Z. Hu, P. Date, A. Li, and W. Jiang. 2023. A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices. IEEE International Conference on Quantum Computing and Engineering (QCE 2023), September 17-22, 2023, Bellevue, WA, 272-282. Piscataway, New Jersey:IEEE.PNNL-SA-187540.doi:10.1109/QCE57702.2023.00038

Kuschel M., T.P. Marrinan, and T. Hasija. 2023. Geodesic-based relaxation for deep canonical correlation analysis. IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP 2023), September 17-20, 2023, Rome, Italy, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-188975.doi:10.1109/MLSP55844.2023.10285937

Peng H., S. Huang, T. Zhou, Y. Luo, C. Wang, Z. Wang, and J. Zhao, et al. 2023. AutoReP: Automatic ReLU Replacement for Fast Private Network Inference. IEEE/CVF International Conference on Computer Vision (ICCV 2023), October 1-6, 2023, Paris, France, 5155-5165. Piscataway, New Jersey:IEEE.PNNL-SA-187458.doi:10.1109/ICCV51070.2023.00478

Henderson N., M. Adham, R. Bass, and T. Slay. 2023. Protecting Customer Privacy Through Distributed Energy Resource Anonymization. Proceedings of the IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-179903.doi:10.1109/PESGM52003.2023.10253065

Arnheim J., T. Mortlock, H. Fathima, P. Pataranutaporn, N. Ahmed, A. Tbaileh, and T. Parhizkar. 2023. Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios. Proceedings of the IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-179932.doi:10.1109/PESGM52003.2023.10252367

Zhou Y., H. Jenne, D.R. Brown, M.R. Shapiro, B.A. Jefferson, C.A. Joslyn, and G. Roek, et al. 2023. Comparing Mapper Graphs of Artificial Neuron Activations. Topological Data Analysis and Visualization (TopoInVis 2023), October 22-23, 2023, Melbourne, Australia, 41-50. Los Alamitos, California: IEEE Computer Society.PNNL-SA-186740.doi:10.1109/TopoInVis60193.2023.00011

Nettasinghe D., S. Chatterjee, R. Tipireddy, and M. Halappanavar. 2023. Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti’s Theorem for Markov Chains. Proceedings of Machine Learning Research: 40th International Conference on Machine Learning (ICML 2023), July 23-29, 2023, Honolulu, HI, edited by A. Krause, et al, 25890--25903. Maastricht:ML Research Press.PNNL-SA-178224.

Dutta A., E. Al-Shaer, S. Chatterjee, and Q. Duan. 2023. Autonomous Cyber Defense Against Dynamic Multi-strategy Infrastructural DDoS Attacks. IEEE Conference on Communications and Network Security (CNS 2023), October 2-5, 2023, Orlando, FL, 1-9. Piscataway, New Jersey:IEEE.PNNL-SA-189781.doi:10.1109/CNS59707.2023.10288937

Dutta S., H. Naghibijouybari, A. Gupta, N. Abu-Ghazaleh, A. Marquez, and K.J. Barker. 2023. Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU System. Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA '23), June 17--21, 2023, Orlando, FL, 1-13, Art. No. 45. New York, New York:Association for Computing Machinery.PNNL-SA-184963.doi:10.1145/3579371.3589080

Mukherjee S., R. Hossain, Y. Liu, W. Du, V.A. Adetola, S. Mohiuddin, and Q. Huang, et al. 2023. Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning. IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-178010.doi:10.1109/PESGM52003.2023.10252480

Richards L.E., E. Raff, and C. Matuszek. 2023. Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition. Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security co-located with the 30th ACM Conference on Computer and Communications Security, November 30, 2023, Copenhagen, Denmark, 161–171. New York, New York:Association for Computing Machinery.PNNL-SA-191638.doi:10.1145/3605764.3623911

D'Onofrio A., A. Hossain, L. Santana, N. Machlovi, S.A. Stein, J. Liu, and A. Li, et al. 2023. Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System. IEEE International Conference on Big Data (BigData 2023), December 15-18, 2023, Sorrento, Italy, 221-228. Piscataway, New Jersey:IEEE.PNNL-SA-192282.doi:10.1109/BigData59044.2023.10386676

Francis C.B., S. Poudel, A. Veeramany, and A.P. Reiman. 2023. A Novel Ranking Algorithm for Topology Identification in Power Distribution Systems. IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-179785.doi:10.1109/PESGM52003.2023.10252471

Abhyankar S.G., J. Drgona, A.R. Tuor, and A.J. August. 2023. Neuro-physical dynamic load modeling using differentiable parametric optimization. IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-179235.doi:10.1109/PESGM52003.2023.10253098

Jain M., X. Sun, S. Datta, and A. Somani. 2023. A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events. IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-179891.doi:10.1109/PESGM52003.2023.10252752

Luo Y., C. Tan, N. Bohm Agostini, A. Li, A. Tumeo, N. Dave, and T. Geng. 2023. ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs. Proceedings of the 60th ACM/IEEE Design Automation Conference (DAC 2023), July 9-13, 2023, San Franciso, CA, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-180015.doi:10.1109/DAC56929.2023.10247873

Subasi O., J.B. Manzano Franco, and K.J. Barker. 2023. Denial of Service Attack Detection via Differential Analysis of Generalized Entropy Progressions. IEEE International Conference on Cyber Security and Resilience (CSR 2023), July 31-August 2, 2023, Venice, Italy, 219-226. Piscataway, New Jersey:IEEE.PNNL-SA-182995.doi:10.1109/CSR57506.2023.10224957

Burghardt J.A., A.M. Tartakovsky, K. Xu, and E. Darve. 2023. Autonomous Inversion of In Situ Deformation Measurement Data for Injection-Induced Stress Change. Proceedings of the 57th U.S. Rock Mechanics/Geomechanics Symposium, June 25-28, 2023, Atlanta, Georgia, Paper No. ARMA-2023-0773. Alexandria, Virginia:American Rock Mechanics Association (ARMA).PNNL-SA-182979.doi:10.56952/ARMA-2023-0773

Pilet T.J., and T. Rakha. 2023. Predicting Building Envelope Construction from In-Situ Thermal Testing. Building Performance Analysis Conference and SimBuild, (IBPSA 2022), September 14-16, 2022, Chicago, IL, 2022, 158 - 164. Peachtree Corners, Georgia:American Society of Heating Refrigerating and Air-Conditioning Engineers (ASHRAE).PNNL-SA-169565.

Ward L., J. Pauloski, V. Hayot-Sasson, R. Chard, Y. Babuji, G. Sivaraman, and S. Choudhury, et al. 2023. Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources. IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW 2023), May 15-19, 2023, St. Petersburg, FL, 32-41. Piscataway, New Jersey:IEEE.PNNL-SA-179856.doi:10.1109/IPDPSW59300.2023.00018

Brown D.R., and H.J. Kvinge. 2023. Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2023), June 18-22, 2023, Vancouver, Canada, 620-627. Los Alamitos, California: IEEE Computer Society.PNNL-SA-182893.doi:10.1109/CVPRW59228.2023.00069

Kwon K., S. Mukherjee, H. Zhu, and T. Vu. 2023. Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control. In Proceedings of the American Control Conference (ACC 2023), May 31- June 2, 2023, San Diego, CA, 4483-4488. Piscataway, New Jersey: IEEE.PNNL-SA-177914. doi:10.23919/ACC55779.2023.10156618

Liu Z., Y. Yang, Z. Pan, A. Sharma, A. Hasan, C. Ding, and A. Li, et al. 2023. Ising-CF: A Pathbreaking Collaborative Filtering Method Through Efficient Ising Machine Learning. In Proceedings of the 60th Design Automation Conference (DAC 2023), July 9-13, 2023, San Francisco, CA, 1-6. Piscataway, New Jersey: IEEE.PNNL-SA-182186. doi:10.1109/DAC56929.2023.10247860

Haghi P., W. Krska, C. Tan, T. Geng, P. Chen, C. Greenwood, and A. Guo, et al. 2023. FLASH: FPGA-Accelerated Smart Switches with GCN Case Study. In Proceedings of the 37th International Conference on Supercomputing (ICS-2023), June 21-23, 2023, Orlando, FL, edited by 450–462. New York, New York: Association for Computing Machinery.PNNL-SA-167625. doi:10.1145/3577193.3593739

Pan Z., A. Sharma, J. Hu, Z. Liu, A. Li, H. Liu, and M. Huang, et al. 2023. Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. In Proceedings of the AAAI Conference on Artificial Intelligence, February 7-14, 2023, Washington, D.C., 37, 9354-9363. Washington, District Of Columbia: AAAI Press.PNNL-SA-176751. doi:10.1609/aaai.v37i8.26121

Li S., J. Drgona, S.G. Abhyankar, and L. Pileggi. 2023. Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning. In e-Energy '23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, June 20-23, 2023, Orlando, FL, 106–114. New York, New York: Association for Computing Machinery.PNNL-SA-184952. doi:10.1145/3599733.3600257

Khatir M., N. Choudhary, S. Choudhury, K. Agarwal, and C. Reddy. 2023. A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, August 19-25, 2023, Macao, edited by E. Elkind, 2023, 3875-3883. Red Hook, New York: International Joint Conferences on Artificial Intelligence (IJCAI); Curran Associates, Inc.PNNL-SA-173369. doi:10.24963/ijcai.2023/431

Dong W., G. Kestor, and D. Li. 2023. Auto-HPCnet: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications. In Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2023), June 16-23, 2023, Orlando, FL, 31–44. New York, New York: Association for Computing Machinery.PNNL-SA-185009. doi:10.1145/3588195.3592985

Nghiem T., J. Drgona, C. Jones, Z. Nagy, R. Schwan, B. Dey, and A. Chakrabarty, et al. 2023. Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. In American Control Conference (ACC 2023), May 31-June 2, 2023, San Diego, CA, 3735-3750. Piscataway, New Jersey: IEEE.PNNL-SA-183234. doi:10.23919/ACC55779.2023.10155901

Tharzeen A., S. Munikoti, P. Prakash, J. Kim, and B. Natarajan. 2023. A General Spatiotemporal Imputation Framework for Missing Sensor Data. In IEEE Conference on Artificial Intelligence (CAI 2023), June 5-6, 2023, Santa Clara, CA, 55-58. Los Alamitos, California: IEEE Computer Society.PNNL-SA-183063. doi:10.1109/CAI54212.2023.00032

Purvine E., D.R. Brown, B.A. Jefferson, C.A. Joslyn, B.L. Praggastis, A. Rathore, and M.R. Shapiro, et al. 2023. Experimental Observations of the Topology of Convolutional Neural Network Activations. In Proceedings of the 37th Conference on Artificial Intelligence, (AAAI 2023), February 7-14, 2023, Washington D.C., 37, 9470 - 9479. Washington, District of Columbia: AAAI Press.PNNL-SA-176623. doi:10.1609/aaai.v37i8.26134

Bretas A., and A. Dutta. 2023. Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework. In IEEE Kansas Power and Energy Conference (KPEC 2023), April 27-28, 2023, Manhattan, KS, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-181923. doi:10.1109/KPEC58008.2023.10215452

Glass S.W., M.P. Spencer, M.S. Prowant, A. Sriraman, J. Son, and L.S. Fifield. 2023. The ARENA Test Bed – A Versatile Resource for I&C Development and Validation. In Proceedings of the 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023), July 15-20, 2023, Knoxville, TN, 1325-1334. Downers Grove, Illinois: American Nuclear Society.PNNL-SA-182564.

Baweja J.A., B.A. Jefferson, and C. Fallon. 2023. Developing Confidence in Machine Learning Results. In Human Factors and Simulation. International Conference on Applied Human Factors and Ergonomics (AHFE 2023), July 20-24, 2023, San Francisco, CA, edited by J. Wright and D. Barber, 83, 184–191. New York, New York: Applied Human Factors and Ergonomics International.PNNL-SA-181613.doi:10.54941/ahfe1003576

Wenskovitch J.E., and A.D. Jaodand. 2023. Human Factors for Machine Learning in Astronomy. In Human Factors and Simulation. International Conference on Applied Human Factors and Ergonomics (AHFE 2023), July 20-24, 2023, San Francisco, CA, edited by J. Wright and D. Barber, 83, 226-235. New York, New York: Applied Human Factors and Ergonomics International.PNNL-SA-182236. doi:10.54941/ahfe1003580

Shaw Cortez W.E., S.S. Vasisht, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2023. Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. In 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), January 4-6, 2023, Canberra, Australia. IFAC-Papers Online, edited by J. Trumpf and R. Mahony, 56, 228-233. Amsterdam: Elsevier.PNNL-SA-175459. doi:10.1016/j.ifacol.2023.02.039

Guo A., Y. Hao, C. Wu, P. Haghi, Z. Pan, M. Si, and D. Tao, et al. 2023. Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training. In Proceedings of the 27th International Conference on Supercomputing (ICS-2023) June 21-23, 2023, Orlando, FL, 336–347. New York, New York: Association for Computing Machinery.PNNL-SA-181666. doi:10.1145/3577193.3593724

Hyder B., A. Ahmed, P.T. Mana, T.W. Edgar, and S. Niddodi. 2023. Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids. In 11th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES 2023), May 9, 2023, San Antonio, TX, 1-6. Piscataway, New Jersey: IEEE.PNNL-SA-182076. doi:10.1109/MSCPES58582.2023.10123428

Truong L.T., W. Choi, C.L. Wight, E.D. Coda, T.H. Emerson, K.S. Kappagantula, and H.J. Kvinge. 2023. Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing. In TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, March 18-23, 2023, San Diego, CA, 587-595. Pittsburgh, Pennsylvania: The Minerals, Metals & Materials Society.PNNL-SA-177365. doi:10.1007/978-3-031-22524-6_52

Shah M., R.W. Neff, H. Wu, M. Minutoli, A. Tumeo, and M. Becchi. 2023. Accelerating Random Forest Classification on GPU and FPGA. In Proceedings of the 51st International Conference on Parallel Processing (ICPP 2022), August 29-September 1, 2022, Bordeaux, France, Art. No. 4. New York, New York: Association for Computing Machinery.PNNL-SA-174343. doi:10.1145/3545008.3545067

Castellana V.G., N. Bohm Agostini, A.M. Limaye, V.C. Amatya, M. Minutoli, J.B. Manzano Franco, and A. Tumeo, et al. 2023. Towards On-Chip Learning for Low Latency Reasoning with End-to-End Synthesis. In Proceedings of the 28th Asia and South Pacific Design Automation Conference (ASPDAC 2023), January 16-19, 2023, Tokyo, Japan, 632–638. New York, New York: Association for Computing Machinery.PNNL-SA-179740. doi:10.1145/3566097.3568360

Mudunuru M., B. Ahmmed, L. Frash, and R.M. Frijhoff. 2023. Deep Learning for Modeling Enhanced Geothermal Systems. In Proceedings of the 48th Workshop on Geothermal Reservoir Engineering, February 6-8, 2023, Stanford, CA, Paper No. SGP-TR-224. Stanford, California: Stanford University.PNNL-SA-181520.

Chrisman B.S., M. Varma, S. Maleki, M. Brbic, C.A. Joslyn, and M. Zitnik. 2023. Session Introduction: Graph Representations and Algorithms in Biomedicine. In Pacific Symposium on Biocomputing, January 3-7, 2023, Hawaii, HI. Biocomputing, 2023, 55-60.PNNL-SA-180977. doi:10.1142/9789811270611_0006

Reports

Tipireddy R., V.C. Amatya, W.S. Rosenthal, and M. Subramanian. 2023. Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications Richland, WA: Pacific Northwest National Laboratory.

DeSmet C.N., M.K. Girard, E.D. Coda, and Y.Z. Watkins. 2023. Universal Fourier Attack for Time Series. Richland, WA: Pacific Northwest National Laboratory.

Ortiz Marrero C.M., N. Wiebe, J.C. Furches, and M.J. Ragone. 2023. Quantum Neural Networks: Issues, Training, and Applications. Richland, WA: Pacific Northwest National Laboratory.

Athon M.T., D.K. Ciesielski, J.F. Corbey, S. Hu, E. King, Y. Li, and J.I. Royer, et al. 2023. Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning. Richland, WA: Pacific Northwest National Laboratory.

Chiang T.Y., A.W. Engel, S. Qadeer, M. Vargas, S. Choudhury, and P. Stinis. 2023. Report on Pure Mathematics of Machine Learning. Richland, WA: Pacific Northwest National Laboratory.

Drgona J., A.R. Tuor, J.V. Koch, M.R. Shapiro, E. King, and D.L. Vrabie. 2023. Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST) Richland, WA: Pacific Northwest National Laboratory.

Prange M.P., N. Govind, P. Stinis, E.S. Ilton, and A.A. Howard. 2023. A Multifidelity and Multimodal Machine Learning Approach for Extracting Bonding Environments of Impurities and Dopants from X-ray Spectroscopies Richland, WA: Pacific Northwest National Laboratory.

Ghosh S., M. Jain, H. Lee, and K.J. Roche. 2023. Proxy Applications for Converged Workloads: DMC LDRD Initiative Richland, WA: Pacific Northwest National Laboratory.

Gioiosa R., E. Apra, A. Marquez, A.R. Panyala, R.A. Ashraf, and L. Guo. 2023. Navier: Dataflow Architecture for Computation Chemistry Richland, WA: Pacific Northwest National Laboratory.

Wiedner E.S., B.A. Helfrecht, J.D. Erickson, and N.M. Washton. 2023. Machine Learning for Prediction of Thermodynamic Descriptors Richland, WA: Pacific Northwest National Laboratory.

Thomas D.G., Z.J. Weems, R.E. Overstreet, and B.A. Wilson. 2023. Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection Richland, WA: Pacific Northwest National Laboratory.

Hofer W.J., O. Bel, B. Hyder, M.W. Bruggeman, and T.W. Edgar. 2023. Increased Interpretability for Model-Driven Deception: MARS LDRD Project Richland, WA: Pacific Northwest National Laboratory.

Minutoli M., M. Halappanavar, E. Apra, P.Z. El-Khoury, and N. Govind. 2023. Graman: Graph Network Based Simulator for Forecasting Molecular Polarizability Richland, WA: Pacific Northwest National Laboratory.

Dreslin B.D., and J.A. Baweja. 2023. “Shoulda, Coulda, Woulda”: Conceptualizing the Differences in Trust Between Human-Human Teaming and Human-Machine Teaming Richland, WA: Pacific Northwest National Laboratory.

Taufique M., M.C. Macduff, S. Bai, D.S. McAllester, M.G. Mamun, J. Wang, and M.R. Kieburtz, et al. 2023. Data Archive and Portal (DAP) Platform for Solid Phase Processing Technologies Richland, WA: Pacific Northwest National Laboratory.

Marcial J., J.J. Neeway, C.I. Pearce, L. Nava-Farias, M.J. Schweiger, D.K. Peeler, and C.L. Arendt, et al. 2023. Chemical durability assessment of enhanced low-activity waste glasses through EPA method 1313 Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Madda R., V.A. Petyuk, Y. Wang, T. Shi, C. Shriver, K.D. Rodland, and T. Liu. 2023. Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection. In Serum/Plasma Proteomics: Methods and Protocols. Methods in Moleculary Biology, edited by D.W. Greening and R.J. Simpson. 579–592. New York, New York:Humana.PNNL-ACT-SA-10675. doi:10.1007/978-1-0716-2978-9_33

2022

Journal articles

Huang R., W. Gao, R. Fan, and Q. Huang. 2022. Damping Inter-Area Oscillation using Reinforcement Learning Controlled TCSC. IET Generation, Transmission and Distribution 16, no. 11:2265-2275.PNNL-SA-171507.doi:10.1049/gtd2.12441

Taheri M.L., W. Carter, and B.P. Uberuaga. 2022. On the frontiers of coupled extreme environments. MRS Bulletin 47, no. 11:1104-1112.PNNL-SA-187078.doi:10.1557/s43577-022-00442-y

Yeung Y., D.A. Barajas-Solano, and A.M. Tartakovsky. 2022. Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems. Water Resources Research 58, no. 5:Art. No. e2021WR03103.PNNL-SA-177675.doi:10.1029/2021WR031023

Flynn B., A.A. Gentile, N.O. Wiebe, R. Santagati, and A. Laing. 2022. Quantum Model Learning Agent: Characterization of Quantum Systems Through Machine Learning. New Journal of Physics 24, no. 5:Art. No. 053034.PNNL-SA-179121.doi:10.1088/1367-2630/ac68ff

Roggero A., J. Filipek, S. Chang, and N.O. Wiebe. 2022. Quantum Machine Learning with SQUID. Quantum 6.PNNL-SA-179120.doi:10.22331/Q-2022-05-30-727

Nguyen Q.H., X. Ke, N.A. Samaan, J.T. Holzer, M.A. Elizondo, H. Zhou, and Z. Hou, et al. 2022. Transmission-distribution long-term volt-var planning considering reactive power support capability of distributed PV. International Journal of Electrical Power & Energy Systems 138.PNNL-SA-163369. doi:10.1016/j.ijepes.2022.107955

Narayanan N., Z. Chen, B. Fang, G. Li, K. Pattabiram, and N.A. Debardeleben. 2022. Fault Injection for TensorFlow Applications. IEEE Transactions on Dependable and Secure Computing 20, no. 4:2677 - 2695.PNNL-SA-161122. doi:10.1109/TDSC.2022.3175930

Li J., and A.M. Tartakovsky. 2022. Physics-informed Karhunen-Loeve and Neural Network Approximations for Solving Inverse Differential Equation Problems. Journal of Computational Physics 462.PNNL-SA-178768. doi:10.1016/j.jcp.2022.111230

Liu X., A. Lumsdaine, M. Halappanavar, K.J. Barker, and A. Gebremedhin. 2022. Direction-Optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis. ACM Journal of Experimental Algorithmics 27, no. 2:Art. No. 1.12, pp 1-31.PNNL-SA-163585. doi:10.1145/3564593

Lee J., A. Rahman, S. Huang, A.D. Smith, and S. Katipamula. 2022. On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability. Science and Technology for the Built Environment 28, no. 9:1150-1165.PNNL-SA-166250. doi:10.1080/23744731.2022.2094729

Maruyama B., J. Hattrick-Simpers, W. Musinski, L. Graham-Bradi, K. Li, J. Hollenbach, and A. Sing, et al. 2022. Artificial intelligence for materials research at extremes. MRS Bulletin 47, no. 11:1154-1164.PNNL-SA-181753. doi:10.1557/s43577-022-00466-4

Monteiro R., R. Teixeira, and A. Bretas. 2022. POWER QUALITY DISTURBANCES DIAGNOSIS: A 2D DENSELY CONNECTED CONVOLUTIONAL NETWORK FRAMEWORK. Electric Power Systems Research 212.PNNL-SA-172764. doi:10.1016/j.epsr.2022.108252

Tipireddy R., P. Perdikaris, P. Stinis, and A.M. Tartakovsky. 2022. Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations. Journal of Machine Learning for Modeling and Computing 3, no. 2:23-46.PNNL-SA-177559. doi:10.1615/JMachLearnModelComput.2022041787

Munger J.E., D.P. Herrerra, S.M. Haver, L. Waterhouse, M.F. McKenna, R.P. Dziak, and J. Gedamke, et al. 2022. Machine learning analysis reveals relationship between pomacentrid calls and environmental cues. Marine Ecology Progress Series 681.PNNL-SA-159507. doi:10.3354/meps13912

Glenski M.F., E.M. Ayton, S. Soni, E.G. Saldanha, D.L. Arendt, B. Quiter, and R. Cooper, et al. 2022. Learning Global Proliferation Expertise Evolution using AI-Driven Analytics and Public Information. IEEE Transactions on Nuclear Science 69, no. 6:1375 - 1384.PNNL-SA-164401. doi:10.1109/TNS.2022.3162216

Kalinin S.V., M. Ziatdinov, S.R. Spurgeon, C. Ophus, E.A. Stach, T. Susi, and J. Agar, et al. 2022. Deep Learning for Electron and Scanning Probe Microscopy: From Materials Design to Atomic Fabrication. MRS Bulletin 47, no. 9:931-939.PNNL-SA-171109. doi:10.1557/s43577-022-00413-3

Regier P.J., M.T. Duggan, A.N. Myers-Pigg, and N.D. Ward. 2022. Effects of Random Forest modeling decisions on biogeochemical time series predictions. Limnology and Oceanography Methods.PNNL-SA-169660. doi:10.1002/lom3.10523

Marin Quintero J., C. Orozco-Henao, A. Bretas, J. Velez, A. Herrada, A. Barranco-Carlos, and W. Percybrooks. 2022. Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids. Journal of Modern Power Systems and Clean Energy 10, no. 6:1648 - 1657.PNNL-SA-171019. doi:10.35833/MPCE.2021.000444

Gao P., A. Andersen, J.P. Sepulveda, G.U. Panapitiya, A.M. Hollas, E.G. Saldanha, and V. Murugesan, et al. 2022. SOMAS: a platform for data-driven material discovery in redox flow battery development. Scientific Data 9.PNNL-SA-161978. doi:10.1038/s41597-022-01814-4

Gunnell L., K. Manwaring, X. Lu, J.G. Reynolds, J.D. Vienna, and J. Hedengren. 2022. Machine Learning with Gradient-based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints. Processes 10, no. 11:Art. No. 2365.PNNL-SA-178239. doi:10.3390/pr10112365

Geiss A.V., S.J. Silva, and J.C. Hardin. 2022. Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning. Geoscientific Model Development 15, no. 17:6677–6694.PNNL-SA-172996. doi:10.5194/gmd-15-6677-2022

Yang A., M. Rodriguez, D. Yang, J. Yang, W. Cheng, C. Cai, and H. Qiu. 2022. Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level. Remote Sensing 14, no. 10:Art. No. 2369.PNNL-SA-179217. doi:10.3390/rs14102369

Mudunuru M., V.V. Vesselinov, and B. Ahmmed. 2022. GeoThermalCloud: Machine Learning for Geothermal Resource Exploration. Journal of Machine Learning for Modeling and Computing 3, no. 4:57–72.PNNL-SA-178322. doi:10.1615/JMachLearnModelComput.2022046445

Roy A., M. Taufique, H. Khakurel, R. Devanathan, D.D. Johnson, and G. Balasubramanian. 2022. Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys. npj Materials Degradation 6, no. 1:Art. No. 9.PNNL-SA-170502. doi:10.1038/s41529-021-00208-y

Ramos E.K., C. Tsai, Y. Jia, Y. Cao, M. Manu, R. Taftaf, and A.D. Hoffman, et al. 2022. Machine learning-assisted elucidation of CD81-CD44 interactions in promoting cancer stemness and extracellular vesicle integrity. eLife 11.PNNL-SA-177252. doi:10.7554/eLife.82669

Drgona J., A.R. Tuor, S.S. Vasisht, and D.L. Vrabie. 2022. Dissipative Deep Neural Dynamical Systems. IEEE Open Journal of Control Systems 1.PNNL-SA-174718. doi:10.1109/OJCSYS.2022.3186838

Lewis N., Y. Jin, X. Tang, V. Shah, C.M. Doty, B.E. Matthews, and S.M. Akers, et al. 2022. Forecasting of In Situ Electron Energy Loss Spectroscopy. npj Computational Materials 8.PNNL-SA-175288. doi:10.1038/s41524-022-00940-2

Mudunuru M., K. Son, P. Jiang, G.E. Hammond, and X. Chen. 2022. Scalable Deep Learning for Watershed Model Calibration. Frontiers in Earth Science 10.PNNL-SA-176859. doi:10.3389/feart.2022.1026479

Olszta M.J., D.F. Hopkins, K.R. Fiedler, M.T. Oostrom, S.M. Akers, and S.R. Spurgeon. 2022. An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics. Microscopy and Microanalysis 28, no. 5:1611 - 1621.PNNL-SA-166931. doi:10.1017/S1431927622012065

Watson-Parris D., M. Christensen, A. Laurenson, D. Clewley, E. Gryspeerdt, and P. Stier. 2022. Shipping regulations lead to large reduction in cloud perturbations. Proceedings of the National Academy of Sciences (PNAS) 119, no. 41:Art. No. e2206885119.PNNL-SA-177339. doi:10.1073/pnas.2206885119

Aimone J.B., P. Date, G.A. Fonseca-Guerra, K.E. Hamilton, K. Henke, W.W. Kay, and G. Kenyon, et al. 2022. A Review of Non-Cognitive Applications for Neuromorphic Computing. Neuromorphic Computing and Engineering 2, no. 3:Art. No. 032003.PNNL-SA-171549. doi:10.1088/2634-4386/ac889c

Forzieri G., V. Dakos, N.G. McDowell, R. Alkama, and A. Cescati. 2022. Emerging signals of declining forest resilience under climate change. Nature 608, no. 7923:534–539.PNNL-SA-177011. doi:10.1038/s41586-022-04959-9

Keshava Murthy R., S.J. Dixon, K. Pazdernik, and L.E. Charles. 2022. Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. One Health 15.PNNL-SA-174599. doi:10.1016/j.onehlt.2022.100439

Hagen A.R., K.D. Jarman, J.D. Ward, G.C. Eiden, C.J. Barinaga, E.K. Mace, and C.E. Aalseth, et al. 2022. Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention. Nuclear Instruments and Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1040.PNNL-SA-171868. doi:10.1016/j.nima.2022.167174

Bohm Agostini N., S. Curzel, J. Zhang, A.M. Limaye, C. Tan, V.C. Amatya, and M. Minutoli, et al. 2022. Bridging Python to Silicon: The SODA Toolchain. IEEE Micro 42, no. 5:78 - 88.PNNL-SA-169276. doi:10.1109/MM.2022.3178580

Vesselinov V.V., B. Ahmmed, M. Mudunuru, J.D. Pepin, E.R. Burns, D.L. Siler, and S. Karra, et al. 2022. Discovering Hidden Geothermal Signatures using Non-Negative Matrix Factorization with Customized k-means Clustering. Geothermics 106.PNNL-SA-167650. doi:10.1016/j.geothermics.2022.102576

Nguyen M., B.A. Helfrecht, R.J. Rousseau, and V. Glezakou. 2022. Actinides in Complex Reactive Media: A Combined Ab Initio Molecular Dynamics and Machine Learning Analytics Study of Transuranic Ions in Molten Salts. Journal of Molecular Liquids 365.PNNL-SA-171071. doi:10.1016/j.molliq.2022.120115

Wenzlick M., O. Mamun, R. Devanathan, K.K. Rose, and J.A. Hawk. 2022. Assessment of outliers in alloy datasets using unsupervised techniques. JOM. The Journal of the Minerals, Metals and Materials Society 74, no. 7:2846-2859.PNNL-SA-169146. doi:10.1007/s11837-022-05204-4

Howard A.A., T. Yu, W. Wang, and A.M. Tartakovsky. 2022. Physics-informed CoKriging model of a redox flow battery. Journal of Power Sources 542.PNNL-SA-162807. doi:10.1016/j.jpowsour.2022.231668

Agnew Jr. D., N. Aljohani, R. Mathieu, S. Boamah, K. Nagaraj, J. Mcnair, and A. Bretas. 2022. Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation. Applied Sciences 12, no. 14:Art. No. 6868.PNNL-SA-174282. doi:10.3390/app12146868

Tomlin J., J. Weiss, D.P. Veghte, S. China, M. Fraund, Q. He, and N. Reicher, et al. 2022. Chemical Composition and Morphological Analysis of Atmospheric Particles from an Intensive Bonfire Burning Festival. Environmental Science: Atmospheres 2, no. 4:616-633.PNNL-SA-172265. doi:10.1039/D2EA00037G

Ye Y., M. Strong, Y. Lou, C.A. Faulkner, W. Zuo, and S. Upadhyaya. 2022. Evaluating Performance of Different Generative Adversarial Networks for Large-Scale Building Power Demand Prediction. Energy and Buildings 269.PNNL-SA-166754. doi:10.1016/j.enbuild.2022.112247

Agarwal K., S. Choudhury, S. Tipirneni, P. Mukherjee, C.M. Ham, S. Tamang, and M. Baker, et al. 2022. Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction. Scientific Reports 12.PNNL-SA-166555. doi:10.21203/rs.3.rs-965815/v1

Mohammad-Taheri S., J.D. Zucker, C.T. Hoyt, K. Sachs, V. Tewari, R. Ness, and O. Vitek. 2022. Do-calculus enables estimation of causal effects in partially observed biomolecular pathways. Bioinformatics 38, no. Supplement_1:i350-i358.PNNL-SA-172208. doi:10.1093/bioinformatics/btac251

Forzieri G., V. Dakos, N.G. McDowell, A. Ramdee, and A. Cescatti. 2022. Emerging signals of declining forest resilience under climate change. Nature 608, no. 7923:534–539.PNNL-SA-178330.doi:10.1038/s41586-022-04959-9

Kaul C.M., Z. Hou, H. Zhou, R.K. Rai, and L.K. Berg. 2022. Sensitivity Analysis of Wind and Turbulence Predictions with Mesoscale-Coupled Large Eddy Simulations Using Ensemble Machine Learning. Journal of Geophysical Research: Atmospheres 127, no. 16:Art. No. e2022JD037150.PNNL-SA-160740. doi:10.1029/2022JD037150

Li S., C.A. Powell, S.N. Mathaudhu, B. Gwalani, A. Devaraj, and C. Wang. 2022. Review of recent progress on in situ TEM shear deformation: a retrospective and perspective view. Journal of Materials Science 57, no. 26:12177–12201.PNNL-SA-169397. doi:10.1007/s10853-022-07331-4

Pande P., M.B. Shrivastava, J.E. Shilling, A. Zelenyuk-Imre, Q. Zhang, Q. Chen, and N.L. Ng, et al. 2022. Novel application of machine learning techniques for rapid source apportionment of Aerosol Mass Spectrometer datasets. ACS Earth and Space Chemistry 6, no. 4:932–942.PNNL-SA-167120. doi:10.1021/acsearthspacechem.1c00344

Zhang D., J.M. Comstock, and V.R. Morris. 2022. Comparison of Planetary Boundary Layer Height from Ceilometer with ARM Radiosonde Data. Atmospheric Measurement Techniques 15, no. 16:4735–4749.PNNL-SA-168399. doi:10.5194/amt-15-4735-2022

Wishart D.S., S. Tian, D. Allen, E. Oler, H.M. Peters, V.W. Lui, and V. Gautam, et al. 2022. BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products. Nucleic Acids Research 50, no. W1:W115–W123.PNNL-SA-177349. doi:10.1093/nar/gkac313

Hagos S.M., J. Chen, K.A. Barber, K. Sakaguchi, R.S. Plant, Z. Feng, and H. Xiao. 2022. A Machine-Learning-Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection. Journal of Advances in Modeling Earth Systems 14, no. 7:e2021MS002808.PNNL-SA-162336. doi:10.1029/2021MS002808

Fu Y., W.E. Frazier, K. Choi, L. Li, Z. Xu, V.V. Joshi, and A. Soulami. 2022. Prediction of Grain Structure after Thermomechanical Processing of U-10Mo Alloy using Sensitivity Analysis and Machine Learning Surrogate Model. Scientific Reports 12.PNNL-SA-170738. doi:10.1038/s41598-022-14731-8

Wenskovitch J.E., B.A. Jefferson, A.A. Anderson, J.A. Baweja, D.K. Ciesielski, and C. Fallon. 2022. A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis. Frontiers in Big Data 5.PNNL-SA-171158. doi:10.3389/fdata.2022.897295

Phillips C., L.M. Sheridan, P. Conry, D. Fytanidis, D. Duplyakin, S. Zisman, and N. Duboc, et al. 2022. Evaluation of Obstacle Modelling Approaches for Resource Assessment and Small Wind Turbine Siting: Case Study in the Northern Netherlands. Wind Energy Science 7, no. 3:1153–1169.PNNL-SA-171278. doi:10.5194/wes-7-1153-2022

Starke A., K. Nagaraj, C. Ruben, N. Aljohani, S. Zou, A. Bretas, and J. Mcnair, et al. 2022. Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security. IET Smart Grid.PNNL-SA-171621. doi:10.1049/stg2.12070

Acharya B., B. Ahmmed, Y. Chen, J. Davison, L. Haygood, R.T. Hensley, and R.T. Hensley, et al. 2022. Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. Earth and Space Science 9, no. 4:e2022EA002320.PNNL-SA-172282. doi:10.1029/2022EA002320

Silva S.J., J.C. Hardin, and C.A. Keller. 2022. Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence. Journal of Advances in Modeling Earth Systems 14, no. 4:Art. No. e2021MS002881.PNNL-SA-167899. doi:10.1029/2021MS002881

Ward A.S., A.I. Packman, S. Bernal Berenguer, N. Brekenfeld, J. Drummond, E.B. Graham, and D. Hannah, et al. 2022. Advancing river corridor science beyond disciplinary boundaries with an inductive approach to catalyse hypothesis generation. Hydrological Processes 36, no. 4:Art. No. e14540.PNNL-SA-162331. doi:10.1002/hyp.14540

Zhang S., R. Sadre, B.A. Legg, H. Pyles, T. Perciano, E.W. Bethel, and D. Baker, et al. 2022. Rotational Dynamics and Transition Mechanisms of Surface-Adsorbed Proteins. Proceedings of the National Academy of Sciences of the United States of America 119, no. 16:Art. No. e2020242119. PNNL-SA-171180. doi:10.1073/pnas.2020242119

Kuhbach M., A. London, J. Wang, D.K. Schreiber, F. Mendez-Martin, I. Ghamarian, and H. Bilal, et al. 2022. Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy. Microscopy and Microanalysis 28, no. 4:1038-1053.PNNL-SA-161043. doi:10.1017/S1431927621012241

Hills D.J., J. Damerow, B. Ahmmed, N.K. Catolico, S. Chakraborty, C.M. Coward, and R. Crystal-Ornelas, et al. 2022. Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. Earth and Space Science 9, no. 4:Art. No. e2021EA002108.PNNL-SA-167274. doi:10.1029/2021ea002108

Dwivedi D., C.I. Steefel, B. Arora, J.F. Banfield, J.R. Bargar, J.R. Bargar, and M.I. Boyanov, et al. 2022. From Legacy Contamination to Watershed Systems Science: A Review of Scientific Insights and Technologies Developed through DOE-Supported Research in Water and Energy Security. Environmental Research Letters 17, no. 4:Art. No. 043004.PNNL-SA-161347. doi:10.1088/1748-9326/ac59a9

Wang S., Y. Qian, L. Leung, and Y. Zhang. 2022. Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models. Atmospheric Chemistry and Physics 22, no. 5:3445 - 3468.PNNL-SA-164115. doi:10.5194/acp-22-3445-2022

Abeshu G.W., H. Li, Z. Zhu, Z. Tan, and L. Leung. 2022. Median bed-material sediment particle size across rivers in the contiguous US. Earth System Science Data 14, no. 2:929-942.PNNL-SA-163836. doi:10.5194/essd-14-929-2022

Mamun M.G., M. Taufique, M. Wenzlick, J.A. Hawk, and R. Devanathan. 2022. Uncertainty Quantification for Bayesian Active Learning in Rupture Life Prediction of Ferritic Steels. Scientific Reports 12, no. 1:Art. No. 2083.PNNL-SA-166048. doi:10.1038/s41598-022-06051-8

Roy A., M. Taufique, H. Khakurel, R. Devanathan, D.D. Johnson, and G. Balasubramanian. 2022. Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys. npj Materials Degradation 6, no. 1:Art. No. 9.PNNL-SA-170502. doi:10.1038/s41529-021-00208-y

VerWey J.P. 2022. The Other Artificial Intelligence Hardware Problem. Computer 55, no. 1:34 - 42.PNNL-SA-166996. doi:10.1109/MC.2021.3113271

Hagos S.M., L. Leung, C. Zhang, and K. Balaguru. 2022. An Observationally Trained Markov Model for MJO Propagation. Geophysical Research Letters 49, no. 2:Art. No. e2021GL095663.PNNL-SA-165371. doi:10.1029/2021GL095663

Dixon S.J., R. Keshava Murthy, D.H. Farber, A. Stevens, K. Pazdernik, and L.E. Charles. 2022. A comparison of infectious disease forecasting methods across locations, diseases, and time. Pathogens 11, no. 2:185.PNNL-SA-169380. doi:10.3390/pathogens11020185

Clyde A., S. Galanie, D.W. Kneller, H. Ma, Y. Babuji, B. Blaiszik, and A. Brace, et al. 2022. High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor. Journal of Chemical Information and Modeling 62, no. 1:116–128.PNNL-SA-161210. doi:10.1021/acs.jcim.1c00851

Zhou H., H. Ren, P.D. Royer, H. Hou, and X. Yu. 2022. Big Data Analytics for Long-Term Meteorological Observations at Hanford Site. Atmosphere 13, no. 1:136.PNNL-SA-168627. doi:10.3390/atmos13010136

Doty C., S. Gallagher, W. Cui, W. Chen, S. Bhushan, M.T. Oostrom, and S.M. Akers, et al. 2022. Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data. Computational Materials Science 203.PNNL-SA-164356. doi:10.1016/j.commatsci.2021.111121

Conference papers

Coda E.D., N.C. Courts, C.L. Wight, L.T. Truong, W. Choi, C.W. Godfrey, and T.H. Emerson, et al. 2022. Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning, April 25-29, 2022, Virtual, Online. Maastricht: ML Research Press.PNNL-SA-170544. doi:10.48550/arXiv.2203.08189

Kassab L., S.A. Howland, H.J. Kvinge, K.S. Kappagantula, and T.H. Emerson. 2022. TopTemp: Parsing Precipitate Structure from Temper Topology. In ICML Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG:ML 2022), July 20. 2022, Virtual, Online. Proceedings of Machine Learning Research, edited by A. Cloninger, et al, 196, 199 - 205. Maastricht: ML Research Press.PNNL-SA-170620.

Mukherjee S., S. Nandanoori, S. Guan, K. Agarwal, S. Sinha, S. Kundu, and S. Pal, et al. 2022. Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems. In First Learning on Graphs Conference (LoG 2022), December 9-12, 2022, Virtual, Online. Proceedings of Machine Learning Research, 198, 1-17. Maastricht: ML Research Press.PNNL-SA-173554.

McGuire S.L., S.W. Jackson, T.H. Emerson, and H.J. Kvinge. 2022. Do Neural Networks Trained with Topological Features Learn Different Internal Representations? In Proceedings of Machine Learning Research, 1st Annual NeurIPS Workshop on Symmetry and Geometry in Neural Representations (NeurReps 2022), December 3, 2022, New Orleans, LA, edited by S. Sanborn, et al, 197, 122 - 136. Maastricht: ML Research Press.PNNL-SA-177838.

Wang P., T. Shi, K. Agarwal, S. Choudhury, and C. Reddy. 2022. Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes. In BCB '22: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.PNNL-SA-166400.

Schranz T., J. Exenberger, C.L. Møldrup, J. Drgona, and G. Schweiger. 2022. Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations. In Proceedings of Building Simulation 2021: 17th Conference of IBPSA, September 1-3, 2021, Bruges, Belgium, edited by D. Saelens, et al, 17, 3268 - 3275.PNNL-SA-159867. doi:10.26868/25222708.2021.30806

Courts N.C., and H.J. Kvinge. 2022. Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps. In Proceedings of the Tenth International Conference on Learning Representations (ICLR 2022), April 25-29, 2022, Virtual, Online.PNNL-SA-167143.

Zhang C., T. Geng, A. Guo, J. Tian, M. Herbordt, A. Li, and D. Tao. 2022. H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture. In 32nd International Conference on Field Programmable Logic and Applications (FPL 2022), August 29- September 2, 2022, Belfast, UK, 200-208. Piscataway, New Jersey: IEEE.PNNL-SA-169703. doi:10.1109/FPL57034.2022.00040

Panchal K., S. Das, L.F. De La Torre Quintana, J.H. Miller, R.J. Rallo Moya, and M. Halappanavar. 2022. Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM). In IEEE International Symposium on Technologies for Homeland Security (HST 2022), November 14-15, 2022, Virtual, Online, 1-7. Piscataway, New Jersey: IEEE.PNNL-SA-174297. doi:10.1109/HST56032.2022.10025443

Das S., M. Halappanavar, A. Tumeo, E. Serra, A. Pothen, and E. Al-Shaer. 2022. VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators. In Proceedings of the IEEE International Conference on Big Data (Big Data 2022), December 17-20, 2022, Osaka, Japan, 1224-1229. Piscataway, New Jersey: IEEE.PNNL-SA-170354. doi:10.1109/BigData55660.2022.10020622

Hamad K., N. Aljohani, T. Zou, and A. Bretas. 2022. Prediction of Power Measurements Using Adaptive Filters. In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2022), April 24-28, 2022, New Orleans, LA, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-170338. doi:10.1109/ISGT50606.2022.9817523

Aljohani N., D. Agnew Jr., K. Nagaraj, S. Boamah, R. Mathieu, A. Bretas, and J. McNair, et al. 2022. Cross-Layered Cyber-Physical Power System State Estimation towards a Secure Grid Operation. In IEEE Power & Energy Society General Meeting (PESGM 2022), July 17-21, 2022, Denver, CO, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-170852. doi:10.1109/PESGM48719.2022.9916756

Dixon S.J., T.J. Doster, and T.H. Emerson. 2022. To Fail or not to Fail: An Exploration of Machine Learning Techniques for Predictive Maintenance. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, SPIE Defense + Commercial Sensing, April 3-June 13, 2022, Orlando, FL. Proceedings of the SPIE, edited by T. Pham and L. Solomon, 12113, Paper No. 1211320. Bellingham, Massachusetts: SPIE.PNNL-SA-170742. doi:10.1117/12.2619040

Stein S.A., Y. Mao, J.A. Ang, and A. Li. 2022. QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation. In Proceedings of the 7th ACM/IEEE Symposium on Edge Computing (SEC 2022), December 5-8, 2022, Seattle, WA, 368-374. Piscataway, New Jersey: IEEE.PNNL-SA-178064. doi:10.1109/SEC54971.2022.00054

Mukherjee S., J. Drgona, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. Neural Lyapunov Differentiable Predictive Control. In Proceedings of the 61st IEEE Conference on Decision and Control (CDC 2022), December 6-9, 2022, Cancun, Mexico, 2097-2104. Piscataway, New Jersey: IEEE.PNNL-SA-171659. doi:10.1109/CDC51059.2022.9992386

Mazumdar H., M. Murphy, S. Bhatkande, H.P. Emerson, D.I. Kaplan, and H. Gohel. 2022. Optimized Machine Learning Model for Predicting Groundwater Contamination. In IEEE MetroCon, November 3, 2022, Hurst, TX, 1-3. Piscataway, New Jersey: IEEE.PNNL-SA-178580. doi:10.1109/MetroCon56047.2022.9971133

Spencer M.P., A. Sriraman, S.W. Glass, and L.S. Fifield. 2022. 3D Frequency Domain Reflectometry Digital Twin of an Electrical Cable: A First Glance. In IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP 2022), October 30-November 2, 2022, Denver, CO, 103-106. Piscataway, New Jersey: IEEE.PNNL-SA-175796. doi:10.1109/CEIDP55452.2022.9985387

Jorgenson G.S., H.J. Kvinge, T.H. Emerson, and C.C. Olson. 2022. Random filters for enriching the discriminatory power of topological representations. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning, Proceedings of Machine Learning Research, July 20, 2022, Baltimore, MD, 196, 183-188. Maastricht: ML Research Press.PNNL-SA-172497.

Rawson M.G., and J. Hultgren. 2022. Optimal Transport for Super Resolution Applied to Astronomy Imaging. In Proceedings of the 30th European Signal Processing Conference (EUSIPCO 2022), August 29 - September 2, 2022, Belgrade, 2022-August, 1971 - 1975. Brussels: European Association for Signal Processing.PNNL-SA-174182.

Dilmore R., D. Appriou, D.H. Bacon, C.F. Brown, A. Cihan, E. Gasperikova, and K. Kroll, et al. 2022. Computational Tools and Workflows for Quantitative Risk Assessment and Decision Support for Geologic Carbon Storage Sites: Progress and Insights from the U.S. DOE’s National Risk Assessment Partnership. In Proceedings of the 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16), October 23-24, 2022, Lyon, France. Rochester, New York: Social Science Research Network (SSRN).PNNL-SA-177276. doi:10.2139/ssrn.4298480

Qin C., B. Vyakaranam, P.V. Etingov, M. Venetos, and S. Backhaus. 2022. Machine Learning Based Network Parameter Estimation Using AMI Data. In IEEE Power & Energy Society General Meeting (PESGM 2022), July 17-21, 2022, Denver, CO, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-168243.doi:10.1109/PESGM48719.2022.9917034

Lassetter A.R., K. Mahapatra, D.J. Sebastian Cardenas, S. Gourisetti, J.G. O'Brien, and J.P. Ogle. 2022. Data-Driven PMU Noise Emulation Framework using Gradient-Penalty-Based Wasserstein GAN. In IEEE Power & Energy Society General Meeting (PESGM 2022), July 17-21, 2022, Denver, CO, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-168113. doi:10.1109/PESGM48719.2022.9916787

Sambaturu P., M. Minutoli, M. Halappanavar, A. Kalyanaraman, and A. Vullikanti. 2022. Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), July 23-29, 2022, Vienna, Austria, 5164-5170.PNNL-SA-174461. doi:10.24963

Bohm Agostini N., S. Curzel, A.M. Limaye, V.C. Amatya, M. Minutoli, V.G. Castellana, and J.B. Manzano Franco, et al. 2022. The SODA Approach: Leveraging High-Level Synthesis for Hardware/Software Co-design and Hardware Specialization: Invited. In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC 2022), July 10-14, 2022, San Francisco, CA, 1359–1362. New York, New York: Association for Computing Machinery.PNNL-SA-172445. doi:10.1145/3489517.3530628

Horawalavithana Y.S., E.M. Ayton, S. Sharma, S.A. Howland, M. Subramanian, S.W. Vasquez, and R.J. Cosbey, et al. 2022. Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned. In Proceedings of BigScience Episode #5 -- Workshop on Challenges & Perspectives in Creating Large Language Models, May 2022, Vitrual and Dublin, Ireland, 160–172. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-171279. doi:10.18653/v1/2022.bigscience-1.12

Tan C., T. Tambe, J. Zhang, B. Fang, T. Geng, G. Wei, and D. Brooks, et al. 2022. ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs. In Proceedings of the 36th ACM International Conference on Supercomputing (ICS 2022), June 28-30, 2022, Virtual, Online, Paper No. 4. New York, New York: Association for Computing Machinery.PNNL-SA-172791. doi:10.1145/3524059.3532359

Wenskovitch J.E., A.A. Anderson, S. Kincic, C. Fallon, D.K. Ciesielski, J.A. Baweja, and M.C. Mersinger, et al. 2022. Operator Insights and Usability Evaluation of Machine Learning Assistance for Power Grid Contingency Analysis. In Human Factors in Energy: Oil, Gas, Nuclear and Electric Power. AHFE (2022) International Conference., July 24-28, 2022, New York, NY, edited by R. Boring and R. McDonald, 54, 40-48. New York, New York: AHFE International.PNNL-SA-170435. doi:10.54941/ahfe1002219

Koch L., S. Oesch, A. Chaulagain, M. Adkisson, S.H. Erwin, and W. Brian. 2022. Toward the Detection of Polyglot Files. In Proceedings of the 15th Workshop on Cyber Security Experimentation and Test (CSET 2022), August 8, 2022, Virtual, Online, 120–128. New York, New York: Association for Computing Machinery.PNNL-SA-173385. doi:10.1145/3546096.3546106

Bartoldson B., R. Wang, Y. Fu, D. Widemann, S. Nguyen, J. Bao, and Z. Xu, et al. 2022. Latent Space Simulation for Carbon Capture Design Optimization. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, February 22- March 1, 2022, Virtual, Online, 36, 12447-12453. Palo Alto, California: AAAI Press.PNNL-SA-166438. doi:10.1609/aaai.v36i11.21511

Visweswara Sathanur A., and M.H. Khan. 2022. Scalable Approaches to Selecting Key Entities in Large Networked Infrastructure Systems. In IEEE International Conference on Big Data (Big Data 2021), December 15-18, 2021, Orlando, FL, 1731-1738. Piscataway, New Jersey: IEEE.PNNL-SA-168122. doi:10.1109/BigData52589.2021.9671900

Reports

Kim D., A.J. August, H.J. Kvinge, and J. Evans. 2022. Structures via Reasoning - Applying AI to Cryo Electron Microscopy to Reveal Structural Variability Richland, WA: Pacific Northwest National Laboratory.

Varga T., S.M. Colby, S. China, and A. Battu. 2022. Machine learning approaches to streamline and enhance the analysis of multiscale imaging data for bioaerosol and soil particles Richland, WA: Pacific Northwest National Laboratory.

Wu B., J. Lu, D. Liu, H. Zhou, Z. Hou, Z. Deng, and J. Xiao. 2022. Machine Learning Software for Cylindrical Battery Design and Performance Prediction Richland, WA: Pacific Northwest National Laboratory.

Varikoti R.A., K.J. Schultz, M. Zhou, C. Kombala Nanayakkara Thambiliya, K.R. Brandvold, A. Kruel, and N. Kumar. 2022. Machine Learning-driven Molecular Design for Therapeutic Discovery Richland, WA: Pacific Northwest National Laboratory.

Howland S.A., J.L. Yaros, and N. Kono. 2022. MetaText: Compositional Generalization in Deep Language Models Richland, WA: Pacific Northwest National Laboratory.

Peles A., S.A. Whalen, and G.J. Grant. 2022. Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing Richland, WA: Pacific Northwest National Laboratory.

Oostrom M.T., R.M. Eichler West, M.Y. Obiri, M. Muniak, P. Pande, S.M. Akers, and T. Mao, et al. 2022. Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images Richland, WA: Pacific Northwest National Laboratory.

Lumsdaine A. 2022. Scalable Second Order Optimization for Machine Learning Richland, WA: Pacific Northwest National Laboratory.

Fan X., J.P. Ogle, J.V. Cree, D. Wang, Y. Chen, E.S. Peterson, and T. Fu, et al. 2022. Technical Characterization and Benefit Evaluation of 5G-Enabled Grid Data Transport and Applications Richland, WA: Pacific Northwest National Laboratory.

Chen X., L. Leung, S. Wang, and Z. Duan. 2022.Understanding the Physics Representation of Deep Learning Models in Environmental Applications Richland, WA: Pacific Northwest National Laboratory.

Biswas S., J.D. Follum, E.S. Andersen, and J.S. Banning. 2022. Big Data Analysis of Synchrophasor Data: Outcomes of Research Activities Supported by DOE FOA 1861 Richland, WA: Pacific Northwest National Laboratory.

Ren H., X. Yu, X. Lin, H. Hou, J. Son, H. Zhou, and P.D. Royer. 2022. Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Chikkagoudar S., S. Chatterjee, R. Bharadwaj, A. Ganguly, S. Kompella, and D.E. Thorsen. 2022. Assurance by Design for Cyber Physical Data-Driven Systems. In IoT for Defense and National Security, edited by R. Douglass, et al. 191 - 212. Hoboken, New Jersey: John Wiley & Sons, Inc.PNNL-SA-171061. doi:10.1002/9781119892199.ch11

Kollias L., G.B. Collinge, D. Zhang, S.I. Allec, P. Gurunathan, G. Piccini, and S.F. Yuk, et al. 2022. Assessing entropy for catalytic processes at complex reactive interfaces. In Annual Reports in Computational Chemistry, edited by D.A. Dixon. 3 - 51. Amsterdam: Elsevier. PNNL-SA-174077. doi:10.1016/bs.arcc.2022.09.004

2021

Journal articles

Alexander F.J., J.A. Ang, J.A. Bilbrey, J. Balewski, T. Casey, R. Chard, and J. Choi, et al. 2021. Co-design Center for Exascale Machine Learning Technologies (ExaLearn). The International Journal of High Performance Computing Applications 35, no. 6:598-616.PNNL-SA-156070. doi:10.1177/10943420211029302

Moses I., R. Joshi, B. Ozdemir, N. Kumar, J. Eickholt, and V. Barone. 2021. Machine Learning Screening of Metal-ion Battery Electrode Materials. ACS Applied Materials & Interfaces 13, no. 45:53355–53362.PNNL-SA-160595. doi:10.1021/acsami.1c04627

Zheng Z., M. West, L. Zhao, P. Ma, X. Liu, and N. Riemer. 2021. Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model. Atmospheric Chemistry and Physics 21, no. 23:17727 - 17741.PNNL-SA-172111. doi:10.5194/acp-21-17727-2021

Ortiz Marrero C.M., N.O. Wiebe, and M. Kieferova. 2021. Entanglement-Induced Barren Plateaus. PRX Quantum 2, no. 4:Art. No. 040316.PNNL-SA-157287. doi:10.1103/PRXQuantum.2.040316

Crawford A.J., D. Choi, P.J. Balducci, V.R. Subramanian, and V.V. Viswanathan. 2021. Lithium-ion battery physics and statistics-based state of health model. Journal of Power Sources 501.PNNL-SA-160094. doi:10.1016/j.jpowsour.2021.230032

Nguyen M., R.J. Rousseau, P.D. Paviet, and V. Glezakou. 2021. Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study. ACS Applied Materials & Interfaces 13, no. 45:53398 - 53408.PNNL-SA-163267. doi:10.1021/acsami.1c11358

Sun X., X. Li, S. Datta, X. Ke, Q. Huang, R. Huang, and Z. Hou. 2021. Smart sampling for reduced and representative power system scenario selection. IEEE Open Access Journal of Power and Energy 8.PNNL-SA-159391. doi:10.1109/OAJPE.2021.3093278

Moghaddam M., T.P. Ferre, X. Chen, K. Chen, X. Song, and G. Hammond. 2021.Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?. Water 13, no. 20:Art. No. 2837.PNNL-SA-151168. doi:10.3390/w13202837

Joshi R., and N. Kumar. 2021. Artificial Intelligence for Autonomous Molecular Design: A Perspective. Molecules 26, no. 22:Art. No. 6761.PNNL-SA-159775. doi:10.3390/molecules26226761

Gao P., X. Yang, Y. Tang, M. Zheng, A. Andersen, V. Murugesan, and A.M. Hollas, et al. 2021. Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery. Physical Chemistry Chemical Physics 23, no. 43:24892-24904.PNNL-SA-161057. doi:10.1039/D1CP04475C

Akers S.M., E.J. Kautz, A. Trevino-Gavito, M.J. Olszta, B.E. Matthews, L. Wang, and Y. Du, et al. 2021. Rapid and Flexible Segmentation of Electron Microscopy Data Using Few-Shot Machine Learning. npj Computational Materials 7.PNNL-SA-160786. doi:10.1038/s41524-021-00652-z

Tetef S., N. Govind, and G.T. Seidler. 2021. Unsupervised Machine Learning for Unbiased Chemical Classification in X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy. Physical Chemistry Chemical Physics 23, no. 41:23586-23601.PNNL-SA-163242. doi:10.1039/D1CP02903G

Ren H., X. Song, Y. Fang, Z. Hou, and T.D. Scheibe. 2021. Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River. Frontiers in Artificial Intelligence 4.PNNL-SA-158855. doi:10.3389/frai.2021.648071

Srinivasan S., D. O'Malley, M. Mudunuru, M. Sweeney, J.D. Hyman, S. Karra, and L. Frash, et al. 2021. A machine learning framework for rapid forecasting and history matching in unconventional reservoirs. Scientific Reports 11, no. 1:Ar. No. 21730.PNNL-SA-159040.doi:10.1038/s41598-021-01023-w

Baskaran A., E.J. Kautz, A. Chowdhury, W. Ma, B. Yener, and D.J. Lewis. 2021. Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective. JOM. The Journal of the Minerals, Metals and Materials Society 73, no. 11:3639–3657.PNNL-SA-159271. doi:10.1007/s11837-021-04805-9

St. John J., C. Herwig, D. Kafkes, J. Mitrevski, W. Pellico, G. Perdue, and A. Quintero-Parra, et al. 2021. Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster. Physical Review Accelerators and Beams 24, no. 10:Article No. 104601.PNNL-SA-157642. doi:10.1103/PhysRevAccelBeams.24.104601

Mukherjee S., and T. Vu. 2021. On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee. IEEE Control Systems Letters 5, no. 5:1615-1620.PNNL-SA-155641. doi:10.1109/LCSYS.2020.3041218

Khakurel H., M. Taufique, A. Roy, G. Balasubramanian, G. Ouyang, J. Cui, and D.D. Johnson, et al. 2021. Machine Learning Assisted Prediction of the Young’s Modulus of Compositionally Complex Alloys. Scientific Reports 11, no. 1:17149.PNNL-SA-158789. doi:10.1038/s41598-021-96507-0

Yaman M.Y., K.N. Guye, M. Ziatdinov, H. Shen, D. Baker, S.V. Kalinin, and D.S. Ginger. 2021. INBOUND APPOINTEE PUBLICATION Alignment of Au Nanorods Along de novo Designed Protein Nanofibers Studied with Automated Image Analysis. Soft Matter 17, no. 25:6109-6115.PNNL-SA-162271. doi:10.1039/d1sm00645b

Cui W., X. Dong, B. Xi, and Z. Feng. 2021. Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method. Journal of Climate 34, no. 17:7257–7276.PNNL-SA-163528. doi:10.1175/JCLI-D-20-0862.1

Drgona J., A.R. Tuor, V. Chandan, and D.L. Vrabie. 2021. Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics. Energy and Buildings 243.PNNL-SA-166470. doi:10.1016/j.enbuild.2021.110992

Lu X., I. Sargin, and J.D. Vienna. 2021. Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning. Journal of the American Ceramic Society 104, no. 11:5636-5647.PNNL-SA-159789. doi:10.1111/JACE.17983

Riihimaki L., X. Li, Z. Hou, and L.K. Berg. 2021. Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning. Solar Energy 225. PNNL-SA-164671. doi:10.1016/j.solener.2021.07.047

O'Neill S., M. Diao, S. Raffuse, M. Al-Hamdan, M. Barik, Y. Jia, and S. Reid, et al. 2021. A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires. Journal of the Air and Waste Management Association 71, no. 7:791-814.PNNL-SA-159422. doi:10.1080/10962247.2021.1891994

Wang S., Y. Qian, L. Leung, and Y. Zhang. 2021. Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation. Earth's Future 9, no. 6:e2020EF001910.PNNL-SA-162325. doi:10.1029/2020EF001910

Tian S., X. Cao, R. Greiner, C. Li, A. Guo, and D. Wishart. 2021. CyProduct: A software tool for accurately predicting the byproducts of human cytochrome P450 metabolism. Journal of Chemical Information and Modeling 61, no. 6:3128-3140.PNNL-SA-160965. doi:10.1021/acs.jcim.1c00144

Yao Y., P. Ciais, N. Viovy, W. Li, H. Yang, E. Joetzjer, and B. Bond-Lamberty. 2021. A data-driven global soil heterotrophic respiration dataset and the drivers of its inter-annual variability. Global Biogeochemical Cycles 35, no. 8:e2020GB006918.PNNL-SA-150334. doi:10.1029/2020GB006918

Ahmmed B., S. Karra, V.V. Vesselinov, and M. Mudunuru. 2021. Machine Learning to Discover Mineral Trapping Signatures due to CO2 Injection. International Journal of Greenhouse Gas Control 109.PNNL-SA-158891. doi:10.1016/j.ijggc.2021.103382

Siler D.L., J.D. Pepin, V.V. Vesselinov, M. Mudunuru, and B. Ahmmed. 2021. Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada. Geothermal Energy 9, no. 1:Article No. 17.PNNL-SA-158884. doi:10.1186/s40517-021-00199-8

Silva S.J., P. Ma, J.C. Hardin, and D.A. Rothenberg. 2021. Physically Regularized Machine Learning Emulators of Aerosol Activation. Geoscientific Model Development 14, no. 5:3067-3077.PNNL-SA-158539. doi:10.5194/gmd-14-3067-2021

Zang X., T. Yin, Z. Hou, R.P. Mueller, Z. Deng, and P. Jacobson. 2021. Deep Learning for Automated Detection and Identification of Migrating American eel Anguilla rostrata from Imaging Sonar Data. Remote Sensing 13, no. 14:2671.PNNL-SA-149057. doi:10.3390/rs13142671

Girard M.K., A.R. Hagen, I.J. Schwerdt, M.E. Gaumer, L.W. McDonald, N.O. Hodas, and E.R. Jurrus. 2021. Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis. Journal of Nuclear Materials 552.PNNL-SA-156254. doi:10.1016/j.jnucmat.2021.152983

Mamun M.G., M. Wenzlick, A. Visweswara Sathanur, J.A. Hawk, and R. Devanathan. 2021. Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels. npj Materials Degradation 5, no. 1:20.PNNL-SA-155793. doi:10.1038/s41529-021-00166-5

Krishnamurthy R., R.K. Newsom, L.K. Berg, H. Xiao, P. Ma, and D.D. Turner. 2021. On the estimation of boundary layer heights: A machine learning approach. Atmospheric Measurement Techniques 14, no. 6:4403-4424.PNNL-SA-157374.doi:10.5194/amt-14-4403-2021

Rivas-Ubach A., B.A. Stanfill, S. China, L. Pasa-Tolic, A.B. Guenther, and A.L. Steiner. 2021. Deciphering the source of primary biological aerosol particles: a pollen case study. ACS Earth and Space Chemistry 5, no. 4:969-979.PNNL-SA-157720. doi:10.1021/acsearthspacechem.0c00295

Ahmed A., K.S. Sajan, A.K. Srivastava, and Y. Wu. 2021. Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams. IEEE Transactions on Smart Grid 12, no. 4:3570-3580.PNNL-SA-159464.doi:10.1109/TSG.2021.3054375

Kautz E.J. 2021. Predicting material microstructure evolution via data-driven machine learning. Patterns 2, no. 7:Article No.100285.PNNL-SA-161929. doi:10.1016/j.patter.2021.100285

Webb-Robertson B.M. 2021. Explainable Artificial Intelligence in Endocrinological Medical Research. Journal of Clinical Endocrinology and Metabolism 106, no. 7:e2809 - e2810.PNNL-SA-160901. doi:10.1210/clinem/dgab237

Kalinin S.V., S. Zhang, M. Valleti, H. Pyles, D. Baker, J.J. De Yoreo, and M. Ziatdinov. 2021. Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations. ACS Nano 15, no. 4:6471-6480.PNNL-SA-161461. doi:10.1021/acsnano.0c08914

Wang X., A. Tumeo, J.D. Leidel, J. Li, and Y. Chen. 2021. HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory. IEEE Transactions on Computers 70, no. 6:833 - 848.PNNL-SA-161294. doi:10.1109/TC.2021.3066982

Wang M., J.E. Wenskovitch, L. House, N. Polys, C. North, and C. North. 2021. Bridging Cognitive Gaps Between User and Model in Interactive Dimension Reduction. Visual Informatics 5, no. 2:13-25.PNNL-SA-157445. doi:10.1016/j.visinf.2021.03.002

Peng G.C., M. Alber, A. Buganza Tepole, W.R. Cannon, S. De, S. Dura-Bernal, and K. Garikipati, et al. 2021. Multiscale Modeling Meets Machine Learning: What Can We Learn?. Archives of Computational Methods of Engineering 28, no. 3:1017–1037.PNNL-SA-156360.doi:10.1007/s11831-020-09405-5

Chen X., L. Leung, Y. Gao, and Y. Liu. 2021. Response of US West Coast Mountain Snowpack to Local Sea Surface Temperature Perturbations: Insights from Numerical Modeling and Machine Learning. Journal of Hydrometeorology 22, no. 4:1045-1062.PNNL-SA-157939. doi:10.1175/JHM-D-20-0127.1

Ahmmed B., M. Mudunuru, S. Karra, S.C. James, and V.V. Vesselinov. 2021. A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing. Journal of Computational Physics 432.PNNL-SA-157340. doi:10.1016/j.jcp.2021.110147

Mamun M.G., M. Wenzlick, J.A. Hawk, and R. Devanathan. 2021. A Machine Learning Aided Interpretable Model for Rupture Strength Prediction in Fe-based Martensitic and Austenitic Alloys. Scientific Reports 11, no. 1:5466.PNNL-SA-156871.doi:10.1038/s41598-021-83694-z

Volkova S., M.F. Glenski, E.M. Ayton, E.G. Saldanha, J.A. Mendoza, D.L. Arendt, and Z.H. Shaw, et al. 2021. Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment. Journal of Information Warfare 20, no. 2:42-66.PNNL-SA-154713.

Li H., Y. Yang, H. Wang, B. Li, P. Wang, J. Li, and H. Liao. 2021. Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach. Science of the Total Environment 765.PNNL-SA-156643. doi:10.1016/j.scitotenv.2020.144263

Scruggs C., C. Henkel, C. Stolper, K.A. Cook, and J. Crouser. 2021. Blending Machine Learning and Interaction Design in Audio Explorer. IEEE Computer Graphics and Applications 41, no. 2:89 - 95.PNNL-SA-141120.doi:10.1109/MCG.2019.2950185

Larson K.B., and A.R. Tuor. 2021. Deep learning classification of cheatgrass invasion in the Western United States using biophysical and remote sensing data. Remote Sensing 13, no. 7:Article No. 1246.PNNL-SA-159598. doi:10.3390/rs13071246

Hagen A.R., B.M. Loer, J.L. Orrell, and R.N. Saldanha. 2021. Decision Trees for Optimizing the Minimum Detectable Concentration of Radioxenon Detectors. Journal of Environmental Radioactivity 229.PNNL-SA-154711. doi:10.1016/j.jenvrad.2021.106542

Vassallo D., R. Krishnamurthy, and H. Fernando. 2021. Utilizing Physics-Based Input Features within a Machine Learning Model to Predict Wind Speed Forecasting Error. Wind Energy Science 6, no. 1:295–309.PNNL-SA-153785. doi:10.5194/wes-6-295-2021

Guo M., Q. Zhuang, H. Yao, M. Golub, L. Leung, and Z. Tan. 2021. Validation and Sensitivity Analysis of a 1-D Lake Model across Global Lakes. Journal of Geophysical Research: Atmospheres 126, no. 4:e2020JD033417.PNNL-SA-156424. doi:10.1029/2020JD033417

Cromwell E., P. Shuai, P. Jiang, E. Coon, S.L. Painter, D. Moulton, and Y. Lin, et al. 2021. Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks. Frontiers in Earth Science 9.PNNL-SA-156876. doi:10.3389/feart.2021.613011

Liang Z., J. Wen, Y. Li, J. Chen, Y. Ye, Y. Fu, and W. Livingood. 2021. A review of machine learning in building load prediction. Applied Energy 285.PNNL-SA-152002.doi:10.1016/j.apenergy.2021.116452

Roy S., T. Radivojevic, M. Forrer, J.M. Marti, S.R. Jonnalagadda, T. Backman, and W. Morrell, et al. 2021. Multiomics data collection, visualization, and utilization for guiding metabolic engineering. Frontiers in Bioengineering and Biotechnology 9.PNNL-SA-159094. doi:10.3389/fbioe.2021.612893

Wu H., H. Jia, C. Wang, J. Zhang, and W. Xu. 2021. Recent progress in understanding solid electrolyte interphase on lithium metal anode. Advanced Energy Materials 11, no. 5:2003092.PNNL-SA-155507. doi:10.1002/aenm.202003092

Webb-Robertson B.M., L.M. Bramer, B.A. Stanfill, S.M. Reehl, E.S. Nakayasu, T.O. Metz, and B. Frohnert, et al. 2021. Prediction of the Development of Islet Autoantibodies through Integration of Environmental, Genetic, and Metabolic Markers. Journal of Diabetes 13, no. 2:143-153.PNNL-SA-146734. doi:10.1111/1753-0407.13093

Tartakovsky A.M., D.A. Barajas-Solano, and Q. He. 2021. Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions. Journal of Computational Physics 426.PNNL-SA-149882. doi:10.1016/j.jcp.2020.109904

Lu X., L. Deng, J. Du, J. Du, and J.D. Vienna. 2021. Predicting boron coordination in multicomponent borate and borosilicate glasses using analytical models and machine learning. Journal of Non-crystalline Solids 553.PNNL-SA-155665. doi:10.1016/j.jnoncrysol.2020.120490

Mudunuru M., and S. Karra. 2021. Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing. Computer Methods in Applied Mechanics and Engineering 374.PNNL-SA-157344. doi:10.1016/j.cma.2020.113560

Chiu C., C. Yang, P.J. van Leeuwen, G. Feingold, R. Wood, Y. Blanchard, and F. Mei, et al. 2021. Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques. Geophysical Research Letters 48, no. 2:Article No. e2020GL091236.PNNL-SA-159444. doi:10.1029/2020GL091236

Lawson C., J.M. Marti, T. Radivojevic, S.R. Jonnalagadda, R. Gentz, N.J. Hillson, and S. Peisert, et al. 2021. Machine learning for metabolic engineering: A review. Metabolic Engineering 63.PNNL-SA-158037. doi:10.1016/j.ymben.2020.10.005

Schultz K.J., S.M. Colby, Y. Yesiltepe, J. Nunez, M.Y. McGrady, and R.S. Renslow. 2021. Application and Assessment of Deep Learning for the Generation of Potential NMDA Receptor Antagonists. Physical Chemistry Chemical Physics 23, no. 2:1197-1214.PNNL-SA-152120. doi:10.1039/D0CP03620J

Wang J., S. Huang, D. Wu, and N. Lu. 2021. Operating a commercial building HVAC load as a virtual battery through airflow control. IEEE Transactions on Sustainable Energy 12, no. 1:158-168.PNNL-SA-143915. doi:10.1109/TSTE.2020.2988513

Conference papers

Glenski M.F., E.M. Ayton, R.J. Cosbey, D.L. Arendt, and S. Volkova. 2021. Evaluating Deception Detection Model Robustness To Linguistic Variation. In Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media (SocialNLP 2021), June 10, 2021, Online Workshop, 70-80. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-156922. doi:10.18653/v1/2021.socialnlp-1.6

Jain M., K. Gupta, A. Visweswara Sathanur, V. Chandan, and M. Halappanavar. 2021. Transfer-Learnt Energy Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data. In American Control Conference (ACC 2021), May 25-28, 2021, New Orleans, LA, 2887-2894. Piscataway, New Jersey: IEEE.PNNL-SA-156692. doi:10.23919/ACC50511.2021.9483228

Panapitiya G.U., F.C. Parks, J.P. Sepulveda, and E.G. Saldanha. 2021. Extracting Material Property Measurement Data from Scientific Articles. In Proceedings of the 2021 Conference on Empirical Methods i Natural Language Processing (EMNLP 2021), November 7-11, 2021, Online and Punta Cana, Dominican Republic, 5393-5402. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-162563. doi:10.18653/v1/2021.emnlp-main.438

Kvinge H.J., Z.D. New, N.C. Courts, J.H. Lee, L.A. Phillips, C.D. Corley, and A.R. Tuor, et al. 2021. Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. In AAAI Workshop on Meta-Learning and MetaDL Challenge, February 9, 2021, Virtual, Online. Proceedings of Machine Learning Research, edited by I. Guyon, et al, 140, 77-89. Brookline, Massachusetts: ML Research Press.PNNL-SA-158201.

Lansing C.S., M.S. Levin, C. Sivaraman, R. Fao, and F. Driscoll. 2021. Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond. In OCEANS 2021, September 20-23, San Diego, CA, 1-6. Piscataway, New Jersey: IEEE.PNNL-SA-164526. doi:10.23919/OCEANS44145.2021.9706101

Tymochko S.J., J.A. Chaput, T.J. Doster, E. Purvine, J.T. Warley, and T.H. Emerson. 2021. Con Connections: Detecting Fraud from Abstracts using Topological Data Analysis. In IEEE 20th International Conference on Machine Learning Applications (ICMLA 2021), December 12-16, 2021, Pasadena, CA, edited by M. Arif Wani; I. Sethi; W. Shi; G. Qu; D.S. Raicu and R. Jin, 403-408. Piscataway, New Jersey: IEEE.PNNL-SA-163927. doi:10.1109/ICMLA52953.2021.00069

Vu T., S. Mukherjee, R. Huang, and Q. Huang. 2021. Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems. In IEEE 60th Conference on Decision and Control (CDC 2021), December 14-17, 2021, Austin, TX, 3652-3657. Piscataway, New Jersey: IEEE.PNNL-SA-160849. doi:10.1109/CDC45484.2021.9683573

Fang B., D. Wang, S. Jin, Q. Koziol, Z. Zhang, Q. Guan, and S. Byna, et al. 2021. Characterizing Impacts of Storage Faults on HPC Applications: A methodology and insights. In IEEE International Conference on Cluster Computing (CLUSTER 2021), September 7-10, 2021, Portland, OR, 409-420. Los Alamitos, California: IEEE Computer Society.PNNL-SA-162815.doi:10.1109/Cluster48925.2021.00048

Stein S.A., R. L'Abbate, W. Mu, Y. Liu, B. Baheri, Y. Mao, and Q. Guan, et al. 2021. A Hybrid System for Learning Classical Data in Quantum States. In IEEE International Performance Computing and Communications Conference (IPCCC 2021), October 29-31, 2021, Austin, TX, 1-7. Piscataway, New Jersey: IEEE.PNNL-SA-165589. doi:10.1109/IPCCC51483.2021.9679430

Bel O., S. Mukhopadhyay, N.R. Tallent, F. Faisal Nawab, and D. Long. 2021. WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling. In IEEE International Conference on Big Data (Big Data 2021), December 15-18, 2022, Orlando, FL, edited by Y. Chen, et al, 3031-3041. Piscataway, New Jersey: IEEE.PNNL-SA-168493. doi:10.1109/BigData52589.2021.9671602

Purohit S., P.S. Mackey, J.D. Zucker, A. Bohra, R.D. Deshmukh, and G. Chin. 2021. QLiG: Query Like a Graph For Subgraph Matching. In IEEE Artificial Intelligence & Knowledge Engineering (AIKE 2021), December 1-3, 2021, Laguna Hills, CA, 121-128. Piscataway, New Jersey: IEEE.PNNL-SA-167142. doi:10.1109/AIKE52691.2021.00025

Tian R., L. Guo, J. Li, B. Ren, and G. Kestor. 2021. A High Performance Sparse Tensor Algebra Compiler in MLIR. In IEEE/ACM 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC 2021), November 14, 2021, St. Louis, MO, 27-38. Piscataway, New Jersey: IEEE.PNNL-SA-168094. doi:10.1109/LLVMHPC54804.2021.00009

Biegalski S.R., P. Tsvetkov, Y. Tao, V. Sobes, K. Pazdernik, S. Labov, and D.F. Williams, et al. 2021. 2020 ETI Annual Summer School: Data Science and Engineering. In ASEE Annual Conference and Exposition, July 26-29, 2021, Virtual, Online. Washington, District of Columbia: American Society for Engineering Education.PNNL-SA-160054.

Manu D., Y. Sheng, J. Yang, J. Deng, T. Geng, A. Li, and C. Ding, et al. 2021. FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper. In IEEE/ACM International Conference On Computer Aided Design (ICCAD 2021), November 1-4, 2021, Munich, Germany, 1-7. Piscataway, New Jersey: IEEE.PNNL-SA-166598. doi:10.1109/ICCAD51958.2021.9643440

Wenskovitch J.E., C. Fallon, K. Miller, and A. Dasgupta. 2021. Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking. In IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX 2021), October 24-225, 2021, New Orleans, LA, 40-44. Los Alamitos, California: IEEE Computer Society.PNNL-SA-167141. doi:10.1109/TREX53765.2021.00012

Geng T., C. Wu, C. Tan, C. Xie, A. Guo, P. Haghi, and S. He, et al. 2021. A Survey: Handling Irregularities in Neural Network Acceleration with FPGAs. In IEEE High Performance Extreme Computing Conference (HPEC 2021), September 20-24, 2021, Virtual, Online, 1-8. Piscataway, New Jersey: IEEE.PNNL-SA-165315. doi:10.1109/HPEC49654.2021.9622877

Arendt D.L., Z.H. Shaw, P. Shrestha, E.M. Ayton, M.F. Glenski, and S. Volkova. 2021. CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation. In Workshop on Data Science with Human-in-the-loop: Language Advances (DaSH-LA 2021) colocated with NAACL 2021, June 11, 2021 Virtual, Online, edited by E. Dragut, et al, 79 - 85. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-151151. doi:10.18653/v1/2021.dash-1.13

Fallon C., E. Brayfindley, K.T. Arneson, R.T. Brigantic, L. Kittinger, and M. Stites. 2021. A Methodology for Assessing Risk to Inform Technology Integration. In Proceedings of Resilience Week (RWS 2021), October 18-21, 2021, Salt Lake City, UT. Piscataway, New Jersey: IEEE.PNNL-SA-164014. doi:10.1109/RWS52686.2021.9611794

Mahapatra K., D.J. Sebastian Cardenas, S. Gourisetti, J.G. O'Brien, and J.P. Ogle. 2021. Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements. In IEEE Resilience week (RWS 2021), October 18-21, 2021, Salt Lake City, UT. Piscataway, New Jersey: IEEE.PNNL-SA-166581. doi:10.1109/RWS52686.2021.9611789

Curzel S., N. Bohm Agostini, S. Song, I. Dagli, A.M. Limaye, C. Tan, and M. Minutoli, et al. 2021. Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators. In IEEE/ACM International Conference on Computer Aided Design (ICCAD 2021), November 1-4, 2021, Munich, Germany, 1-7. Piscataway, New Jersey: IEEE.PNNL-SA-166239. doi:10.1109/ICCAD51958.2021.9643474

Vu T., S. Mukherjee, T. Yin, R. Huang, J. Tan, and Q. Huang. 2021. Safe Reinforcement Learning for Emergency Load Shedding of Power Systems. In IEEE Power & Energy Society General Meeting (PESGM 2021), July 26-29, 2021, Washington DC, 1-5. Piscataway, New Jersey:IEEE.PNNL-SA-157689. doi:10.1109/PESGM46819.2021.9638007

Sadnan R., and A. Dubey. 2021. Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems. In IEEE 48th Photovoltaic Specialists Conference (PVSC 2021), June 20-25, 2021, Fort Lauderdale, FL, 1586-1590. Piscataway, New Jersey:IEEE.PNNL-SA-168276. doi:10.1109/PVSC43889.2021.9518472

Baheri B., D. Chen, B. Fang, S.A. Stein, V. Chaudhary, Y. Mao, and S. Xu, et al. 2021. TQEA: Temporal Quantum Error Analysis. In 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S 2021), June 21-24, 2021, Taipei, Taiwan, 65-67. Piscataway, New Jersey: IEEE.PNNL-SA-159972. doi:10.1109/DSN-S52858.2021.00034

Zhang J., N. Bohm Agostini, S. Song, C. Tan, A.M. Limaye, V.C. Amatya, and J.B. Manzano Franco, et al. 2021. Towards Automatic and Agile AI/ML Accelerator Design with End-to-End Synthesis. In IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP 2021), July 7-9, 2021, Virtual, 218-225. Piscataway, New Jersey: IEEE.PNNL-SA-163507. doi:10.1109/ASAP52443.2021.00040

Wang D., J. Bao, Z. Xu, B.J. Koeppel, O.A. Marina, A. Noring, and M. Zamarripa-Perez, et al. 2021. Machine Learning Tools Set for Natural Gas Fuel Cell System Design. In 17th International Symposium on Solid Oxide Fuel Cells (SOF-XVII) July 18, 2021 - July 23, 2021 Stockholm, Sweden. ECS Transactions, 103, Paper No. 2283.PNNL-SA-162378. doi:10.1149/10301.2283ecst

Fujimoto T.C., T.J. Doster, A. Attarian, J.M. Brandenberger, and N.O. Hodas. 2021. The Effect of Antagonistic Behavior in Reinforcement Learning. In AAAI-21 Workshop on Reinforcement Learning in Games, February 8, 2021, Virtual. Menlo Park, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-157666.

King E., C. Bakker, A. Bhattacharya, S. Chatterjee, F. Pan, M.R. Oster, and C.J. Perkins. 2021. Solving the Dynamics-Aware Economic Dispatch Problem with the Koopman Operator. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems (e-Energy '21), June 28-July 2, 2021, Virtual, Online, 137-147. New York, New York: Association for Computing Machinery.PNNL-SA-159920. doi:10.1145/3447555.3464864

Zhou H., Z. Hou, Y. Liu, and P.V. Etingov. 2021. Weather and Random Forest-based Load Profiling Approximation Models and its Transferability across Climate Zones. In Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021), January 4-8, 2021, Virtual, Online, 2020-January, 3321-3328. Los Alamitos, California: IEEE Computer Society.PNNL-SA-154506. doi:10.24251/HICSS.2021.403

Reports

Borders T., M.F. Glenski, T.F. Grimes, J.M. Mendez, B.N. Seiner, A. Sheffield, and A. Shields, et al. 2021. Report on Next-Gen AI for Proliferation Detection Workshop: Domain-Aware Methods Richland, WA: Pacific Northwest National Laboratory.

McDermott J.E., S. Feng, C.H. Chang, D.J. Schmidt, and V.G. Danna. 2021. Structural- and Functional-Informed Machine Learning for Protein Function Prediction Richland, WA: Pacific Northwest National Laboratory.

Suter J.D., J.V. Cree, J.M. Johns, and G. Longoni. 2021. Neural Interactive Machine Learning: Final Report: Compilation of presentation material Richland, WA: Pacific Northwest National Laboratory.

Rivas-Ubach A., S. China, P. Pande, and X. Zheng. 2021. Characterizing the contribution of bioaerosols diversity from complex aerosol particle samples. PNNL-31972. Richland, WA: Pacific Northwest National Laboratory.

Shrivastava M.B., P. Pande, J.E. Shilling, A. Zelenyuk-Imre, and Q.Z. Rasool. 2021. Applying novel analytical tools for analyzing multidimensional secondary organic aerosol measurements Richland, WA: Pacific Northwest National Laboratory.

Pressel K.G., and K. Sakaguchi. 2021. Developing and testing capabilities for simulating cases with heterogeneous land/water surfaces in a novel atmospheric large eddy simulation code Richland, WA: Pacific Northwest National Laboratory.

Baek S., D.H. Bacon, and N.J. Huerta. 2021. NRAP-Open-IAM Analytical Reservoir Model - Development and Testing Richland, WA: Pacific Northwest National Laboratory.

Gourisetti S., M. Mylrea, H.M. Reeve, J.A. Rotondo, G.T. Richards, and J.A. Irwin. 2021. Facility Cybersecurity Framework Best Practices Version 2.0 Richland, WA: Pacific Northwest National Laboratory.

Herling D.R. 2021. Machine Learning for Automated Weld Quality Monitoring and Control - CRADA 461 Richland, WA: Pacific Northwest National Laboratory.

Devanathan R. "Development of Efficient Process for Manufacturing of Thermoplastic Composites with Tailored Properties - CRADA 511." PNNL-SA-160002.

Magnuson J.K. 2021. Application of Machine Learning to Improve Biobased Glucaric Acid Production - CRADA 504 Richland, WA: Pacific Northwest National Laboratory.

Beliaev A.S. 2021. Developing Multi-Gene CRISPRa/I Programs to Accelerate DBTL Cycles in ABF Hosts Engineered for Chemical Production - CRADA 468 Richland, WA: Pacific Northwest National Laboratory.

Vadari M. 2021.Evolving Architectures and Considerations to address Distributed Energy Resources and Non-Wired AlternativesRichland, WA: Pacific Northwest National Laboratory.

2020

Journal articles

Neary V.S., S. Ahn, B.E. Seng, M.N. Allahdadi, T. Wang, Z. Yang, and R. He. 2020. Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment. Journal of Marine Science and Engineering 8, no. 4:Article No. 289.PNNL-SA-152704. doi:10.3390/jmse8040289

Ren H., Z. Hou, Z. Duan, X. Song, W.A. Perkins, M.C. Richmond, and E.V. Arntzen, et al. 2020. Spatial Mapping of Riverbed Grain-size Distribution Using Machine Learning. Frontiers in Water 2.PNNL-SA-151819.doi:10.3389/frwa.2020.551627

Chowdhury A.S., S.M. Reehl, K. Kehn-Hall, B. Bishop, and B.M. Webb-Robertson. 2020. Better Understanding and Prediction of Antiviral Peptides through Primary and Secondary Structure Feature Importance. Scientific Reports 10, no. 1:Article No. 19260.PNNL-SA-154696.doi:10.1038/s41598-020-76161-8

Skomski E., J. Lee, W. Kim, V. Chandan, S. Katipamula, and B.J. Hutchinson. 2020. "Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings." Energy and Buildings 226.PNNL-SA-150099. doi:10.1016/j.enbuild.2020.110350

Vassallo D., R. Krishnamurthy, T. Sherman, and H. Fernando. 2020. Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting. Energies 13, no. 20:5488.PNNL-SA-156586. doi:10.3390/en13205488

Sampath J., S. Alamdari, and J. Pfaendtner. 2020. Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution. Chemistry of Materials 32, no. 19:8043–8059.PNNL-SA-153699. doi:10.1021/acs.chemmater.0c01891

Ma W., E.J. Kautz, A. Baskaran, A. Chowdhury, V.V. Joshi, B. Yener, and D.J. Lewis. 2020. Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships. Journal of Applied Physics 128, no. 13:Article No. 134901.PNNL-SA-153190.doi:10.1063/5.0013720

Tipireddy R., D.A. Barajas-Solano, and A.M. Tartakovsky. 2020. "Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models." Journal of Computational Physics 418.PNNL-SA-142607.

Sun Y., W. Hao, Y. Chen, and B. Liu. 2020. Data-Driven Occupant-Behavior Analytics for Residential Buildings. Energy 206.PNNL-SA-146324. doi:10.1016/j.energy.2020.118100

O'Bryon I., S.C. Jenson, and E.D. Merkley. 2020. Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification. Protein Science 29, no. 9:1864-1878.PNNL-SA-153810. doi:10.1002/pro.3919

Gao P., J. Zhang, Q. Peng, J. Zhang, and V. Glezakou. 2020. General Protocol for the Accurate Prediction of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Augmented DFT. Journal of Chemical Information and Modeling 60, no. 8:3746-3754.PNNL-SA-143566.doi:10.1021/acs.jcim.0c00388

Kautz E.J., W. Ma, S. Jana, A. Devaraj, V.V. Joshi, B. Yener, and D.J. Lewis. 2020. An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction. Materials Characterization 166.PNNL-SA-144375. doi:10.1016/j.matchar.2020.110379

Vassallo D., R. Krishnamurthy, and H. Fernando. 2020. Decreasing Wind Speed Extrapolation Error via Domain-Specific Feature Extraction and Selection. Wind Energy Science 5, no. 3:959–975.PNNL-SA-150381. doi:10.5194/wes-5-959-2020

Hagen A.R., E.D. Church, J.F. Strube, K. Bhattacharya, and V.C. Amatya. 2020. Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs. Journal of Physics: Conference Series 1525.PNNL-SA-143856. doi:10.1088/1742-6596/1525/1/012104

Holden W.M., E.P. Jahrman, N. Govind, and J. Seidler. 2020. Probing Sulfur Chemical and Electronic Structure with Experimental Observation and Quantitative Theoretical Prediction of Ka and Valence-to-Core Kß X-ray Emission Spectroscopy. Journal of Physical Chemistry A 124, no. 26:5415-5434.PNNL-SA-148445. doi:10.1021/acs.jpca.0c04195

Siriwardane E., R. Joshi, N. Kumar, and D. Cakir. 2021. Revealing the Formation Energy–Exfoliation Energy–Structure Correlation of MAB Phases Using Machine Learning and DFT. ACS Applied Materials & Interfaces 12, no. 26:29424–29431.PNNL-SA-151212. doi:10.1021/acsami.0c03536

He Q., D.A. Barajas-Solano, G.D. Tartakovsky, and A.M. Tartakovsky. 2020. Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport. Advances in Water Resources 141.PNNL-SA-149626. doi:10.1016/j.advwatres.2020.103610

Bilbrey J.A., E.F. Ramirez, J.M. Brandi-Lozano, C. Sivaraman, J. Short, I.D. Lewis, and B.D. Barnes, et al. 2020. Improving Radiograph Analysis Throughput through Transfer Learning and Object Detection. Journal of Medical Artificial Intelligence 3.PNNL-SA-149813. doi:10.21037/jmai-20-2

Atkins J., B. Bond-Lamberty, R.T. Fahey, L. Haber, E. Stuart-Haëntjens, B. Hardiman, and E. LaRue, et al. 2020. Application of multidimensional structural characterization to detect and describe moderate forest disturbance. Ecosphere 11, no. 6:Article No. e03156.PNNL-SA-153898. doi:10.1002/ecs2.3156

Trahey L., F. Brushett, N.P. Balsara, G. Ceder, L. Cheng, Y. Chiang, and N.T. Hahn, et al. 2020. Energy storage emerging: A perspective from the Joint Center for Energy Storage Research. Proceedings of the National Academy of Sciences (PNAS) 117, no. 23:12550-12557.PNNL-SA-153667. doi:10.1073/pnas.1821672117

Haridas S., R. Albert, M. Binder, J. Bloem, K.M. LaButti, A. Salamov, and W.B. Andreopoulos, et al. 2020. 101 Dothideomycetes genomes: a test case for predicting lifestyles and emergence of pathogens. Studies in Mycology 96.PNNL-SA-150863. doi:10.1016/j.simyco.2020.01.003

Thomas M., M. Schram, K.M. Fox, J.F. Strube, N.S. Solomon-Oblath, R.J. Rallo Moya, and Z.C. Kennedy, et al. 2020. Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. MRS Advances 5, no. 29-30:1547–1555.PNNL-SA-147390. doi:10.1557/adv.2020.103

Lee J., N.C. Sadler, R.G. Egbert, C.R. Anderton, K.S. Hofmockel, J.K. Jansson, and H. Song. 2020. Deep Learning Predicts Microbial Interactions from Self-organized Spatiotemporal Patterns. Computational and Structural Biotechnology Journal 18.PNNL-SA-150281. doi:10.1016/j.csbj.2020.05.023

Darulova J., S.J. Pauka, N.O. Wiebe, K.W. Chan, G.C. Gardner, M.J. Manfra, and M.C. Cassidy, et al. 2020. Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots. Physical Review Applied 13, no. 5:Article No. 054005.PNNL-SA-158016. doi:10.1103/PhysRevApplied.13.054005

Xue Romeiko X., E. Lee, Y. Sorunmu, and X. Zhang. 2020. Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest. Environmental Science & Technology 54, no. 8:4758–4768.PNNL-SA-150653. doi:10.1021/acs.est.9b06874

Lee E., W. Zhang, X. Zhang, P.R. Adler, S. Lin, B.J. Feingold, and H.A. Khwaja, et al. 2020. Projecting Life-Cycle Environmental Impacts of Corn Production in the U.S. Midwest under Future Climate Scenarios using machine learning approach. Science of the Total Environment 714.PNNL-SA-145315. doi:10.1016/j.scitotenv.2020.136697

Ashtari Esfahani A., S. Boser, N.G. Buzinsky, R. Cervantes, C. Claessens, L. De Viveiros, and M. Fertl, et al. 2020. Cyclotron Radiation Emission Spectroscopy Signal Classification with machine Learning in Project 8. New Journal of Physics 22, no. 3:Article No. 033004.PNNL-SA-146046. doi:10.1088/1367-2630/ab71bd

Huang Q., R. Huang, W. Hao, J. Tan, R. Fan, and Z. Huang. 2020. Adaptive Power System Emergency Control using Deep Reinforcement Learning. IEEE Transactions on Smart Grid 11, no. 2:1171-1182.PNNL-SA-140241. doi:10.1109/TSG.2019.2933191

Hagos S.M., Z. Feng, B. Plant, and A. Protat. 2020. A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds. Journal of Advances in Modeling Earth Systems 12, no. 3:Article No. e2019MS001798.PNNL-SA-144294. doi:10.1029/2019MS001798

Thomas M., M. Schram, K.M. Fox, J.F. Strube, N.S. Solomon-Oblath, R.J. Rallo Moya, and Z.C. Kennedy, et al. 2020. Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems. MRS Advances 5, no. 29-30:1547-1555.PNNL-SA-150139.doi:10.1557/adv.2020.103

Bao J., V. Murugesan, C.J. Kamp, Y. Shao, L. Yan, and W. Wang. 2020. Machine learning coupled multi-scale modeling for redox flow batteries. Advanced Theory and Simulations 3, no. 2:Article No. 1900167.PNNL-SA-148857. doi:10.1002/adts.201900167

Conference papers

Saldanha E.G., A. Garimella, and S. Volkova. 2020 .Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation. In Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), December 15-18, 2020, Virtual, edited by B. Davis, et al, 216 - 226. Stroudsburg, Pennsylvania: Association for Computational Linguistics. PNNL-SA-152898.

Wang N., J.C. Gonzalez Matamoros, L. Babu, and R.A. Fowler. 2020. Retrofit-ability: A supplementary metric to inform energy efficiency policies and programs. In ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 21, 2020. Virtual, 7-440 - 7-454. Washington, District of Columbia: American Council for an Energy-Efficient Economy.PNNL-SA-152256.

Lee J., N.C. Sadler, R.G. Egbert, C.R. Anderton, K.S. Hofmockel, J.K. Jansson, and H. Song. 2020. Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms. In AIChE Annual Meeting, Conference Proceedings, November 16-20, 2020, Virtual, Online, 2020, Paper No. 168750. New York, New York: American Institute of Chemical Engineers.PNNL-SA-158208.

Drgona J., A.R. Tuor, V. Chandan, and D.L. Vrabie. 2020. Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics. In Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop Tackling Climate Change with Machine Learning, December 11, 2020, Virtual. San Diego, California: Neural Information Processing Systems Foundation, Inc.PNNL-SA-156966.

Wu X., Y. Yi, D. Tian, and J. Li. 2020. Generic, Sparse Tensor Core for Neural Networks. In 1st International Workshop on Machine Learning for Software Hardware Co-Design (MLSH 20200), in conjunction with the 29th International Conference on Parallel Architectures and Compilation Techniques (PACT 2020), October 2, 2020, Virtual. Cambridge, Massachusetts: Massachusetts Institute of Technology.PNNL-SA-156246.

Visweswara Sathanur A., and N.A. Baker. 2020. A Clustering-based biased Monte Carlo Approach to Protein Titration Curve Prediction. In IEEE International Conference on Machine Learning and Applications (ICMLA 2020), December 14-17, 2020, Miami, FL, 179-184. Piscataway, New Jersey: IEEE.PNNL-SA-154142. doi:10.1109/ICMLA51294.2020.00037

Ayala A., C. Drazic, B.J. Hutchinson, B.S. Kravitz, and C. Tebaldi. 2020. Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks. In NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning, December 11, 2020.PNNL-SA-157069.

Davis B.F., M.F. Glenski, W.I. Sealy, and D.L. Arendt. 2020. Measure Utility, Gain Trust: Practical Advice for XAI Researchers. In IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX 2020), October 25-30, 2020, Salt Lake City, UT, 1-8. Piscataway, New Jersey:IEEE.PNNL-SA-155588. doi:10.1109/TREX51495.2020.00005

Kandakatla A.R., V. Chandan, S. Kundu, I. Chakraborty, K.A. Cook, and A. Dasgupta. 2020. Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling. In IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX 2020), October 25-30, 2020, Salt Lake City, UT, 16-21. Piscataway, New Jersey: IEEE.PNNL-SA-154767. doi:10.1109/TREX51495.2020.00007

Firoz J.S., A. Li, J. Li, and K.J. Barker. 2020. On the Feasibility of Using Reduced-Precision Tensor Core Operations for Graph Analytics. In IEEE High Performance Extreme Computing Conference (HPEC 2020), September 22-24, 2020, Waltham, MA, 1-7. Piscataway, New Jersey: IEEE.PNNL-SA-153853. doi:10.1109/HPEC43674.2020.9286152

Hou Z., H. Ren, H. Wang, and P.V. Etingov. 2020. "Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system." InIEEE Power & Energy Society General Meeting (PESGM 2020), August 2-6, 2020, Montreal, Canada, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-149280. doi:10.1109/PESGM41954.2020.9281440

Lin X., Z. Hou, Y. Chen, S. Rose, Y. Ma, and F. Pan. 2020."Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest." In IEEE Power & Energy Society General Meeting (PESGM 2020), August 2-6, 2020, Montreal, Canada, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-149108. doi:10.1109/PESGM41954.2020.9281926

Lee J., S. Huang, A. Rahman, A.D. Smith, and S. Katipamula. 2020."Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models." In Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM 2020), November 17, 2020, Yokohama, Japan, 34–38. New York, New York: ACM.PNNL-SA-155449.doi:10.1145/3427773.3427873

Li J., M. Lakshminarasimhan, X. Wu, A. Li, C. Olschanowsky, and K.J. Barker. 2020. A Sparse Tensor Benchmark Suite for CPUs and GPUs. In IEEE International Symposium on Workload Characterization (IISWC 2020), October 27-30, 2020, Beijing, China, 193-204. Piscataway, New Jersey: IEEE.PNNL-SA-142736. doi:10.1109/IISWC50251.2020.00027

Dong W., Z. Xie, G. Kestor, and D. Li. 2020. "Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation." In International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2020), November 9-19, 2020, Atlanta, GA, 1, 879-893. Los Alamitos, California: IEEE Computer Society.PNNL-SA-155468.

Minutoli M., V.G. Castellana, C. Tan, J.B. Manzano Franco, V.C. Amatya, A. Tumeo, and D. Brooks, et al. 2020. SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators. In Proceedings of the 39th International Conference On Computer-Aided Design (ICCAD 2020), November 2-5, 2020, Virtual Conference, edited by Y. Xie, Article No. 98. New York, New York: Association for Computing Machinery.PNNL-SA-155356.doi:10.1145/3400302.3415781

Bel O.M., K. Chang, N.R. Tallent, D. Duellmann, E.L. Miller, F. Faisal Nawab, and D. Long. 2020. "Geomancy: Automated Performance Enhancement through Data Layout Optimization." In Proceedings of the 36th International Conference on Massive Storage Systems and Technology (MSST 2020), October 29-30, 2020, Santa Clara, CA. Santa Cruz, California:Center for Research in Storage Systems, University of California, Santa Cruz.PNNL-SA-155741.

Tan C., C. Xie, A. Li, K.J. Barker, and A. Tumeo. 2020. OpenCGRA: An Open-Source Unified Framework for Modeling,Testing, and Evaluating CGRAs. In IEEE 38th International Conference on Computer Design (ICCD 2020), October 18-21, 2020, 381-388. Piscataway, New Jersey: IEEE.PNNL-SA-152863. doi:10.1109/ICCD50377.2020.00070

Tumeo A., M. Minutoli, V.G. Castellana, J.B. Manzano Franco, V.C. Amatya, D. Brooks, and G. Wei. 2020. Invited: Software defined accelerators from learning tools environment. In Invited: Software defined accelerators from learning tools environment, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-152847.doi:10.1109/DAC18072.2020.9218489

Fulsom B.G. 2020. Bragg curve spectroscopy for improved fission fragment identification. In International Workshop on Fission Product Yields (FPY 2019), 242, Article No. 01006.PNNL-SA-148766. doi:10.1051/epjconf/202024201006

Katz G.E., K. Gupta, and J.A. Reggia. 2020. Reinforcement-based Program Induction in a Neural Virtual Machine. In International Joint Conference on Neural Networks (IJCNN 2020), July 19-24, 2020, Glasgow, UK, 1-8. Piscataway, New Jersey:IEEE.PNNL-SA-155294. doi:10.1109/IJCNN48605.2020.9207671

Hao D., G.R. Asrar, Y. Zeng, Q. Zhu, J. Wen, Q. Xiao, and M. Chen. 2020. DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution. Earth System Science Data 12, no. 3:2209–2221.PNNL-SA-156293. doi:10.5194/essd-12-2209-2020

Alam M.B., O. Ahmed, R.A. Buractaon, A. Hossain, M. Noor-A-Alam, and M.E. Alam. 2020. DIGITAL APPLICATIONS USING REAL-TIME VEHICLE EXHAUST INFORMATION. In 29th International Conference of the International Association For Management of Technology: Towards the Digital World and Industry X.0 (IAMOT 2020), September 13-17, 2020, Cairo, Egypt, 618-627. Pretoria: University of Pretoria.PNNL-SA-152676.

Zou P., A. Li, K.J. Barker, and R. Ge. 2020. Detecting Anomalous Computation with RNNs on GPU-Accelerated HPC Machines. In Proceedings of the 49th International Conference on Parallel Processing (ICPP 2020) August 17-20, 2020, Online., Article No.3404435. New York, New York: Association for Computing Machinery.PNNL-SA-148325.doi:10.1145/3404397.3404435

Rounds J., A.J. Kingsland, M.J. Henry, and K.R. Duskin. 2020. Probing for Artifacts: Detecting Imagenet Model Evasions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020), June 14-19, 2020, Seattle, WA, 3432-3441. Piscataway, New Jersey: IEEE.PNNL-SA-152048. doi:10.1109/CVPRW50498.2020.00403

Truong L.T., C.A. Jones, B.J. Hutchinson, A. August, B.L. Praggastis, R.J. Jasper, and N.M. Nichols, et al. 2020. Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020), June 14-19, 2020, Seattle, WA, 3422-3431. Piscataway, New Jersey: IEEE.PNNL-SA-152069. doi:10.1109/CVPRW50498.2020.00402

Dowling C.P., and B. Zhang. 2020. Transfer Learning for HVAC System Fault Detection. In American Control Conference (ACC 2020), July 1-3, 2020, Denver, CO, 3879-3885. Piscataway, New Jersey:IEEE.PNNL-SA-150637.doi:10.23919/ACC45564.2020.9147772

Liu X., M. Halappanavar, K.J. Barker, A. Lumsdaine, and A. Gebremedhin. 2020. Direction-optimizing Label Propagation and its Application for Community Detection. In Proceedings of the 17th ACM International Conference on Computing Frontiers (CF 2020), June 1-10, 2020. Catania, Italy, 192–201. New York, New York: ACM.PNNL-SA-152667. doi:10.1145/3387902.3392634

Zou P., A. Li, K.J. Barker, and R. Ge. 2020. Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems. In The 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), May 11-144, 2020, Melbourne, Australia, 559-568. Piscataway, New Jersey:IEEE.PNNL-SA-148280. doi:10.1109/CCGrid49817.2020.00-37

Bedoya J.C., C. Liu, and J. Xie. 2020. Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid. In 20th International Conference on Intelligent Systems Applications to Power Systems (ISAP 2019), December 10-14, 2019, New Delhi, India, 56-61. Piscataway, New Jersey: IEEE. PNNL-SA-147189. doi:10.1109/ISAP48318.2019.9065956

Wei H., M.V. Olarte, and G. Goh. 2020. Multiple-objective Reinforcement Learning for Inverse Design and Identification. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI-2020), February 7-12, 2020, New York. Palo Alto, California: AAAI Press.PNNL-SA-147355.

Stinis P. 2020. Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning. In Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS 2020), March 23-25, 2020, Stanford, CA, edited by J. Lee, et al, 2587, Paper No. 5. Aachen: CEUR Workshop Proceedings/RWTH Aachen University.PNNL-SA-143654.

Yang F., Z. Huang, J. Scholtz, and D.L. Arendt. 2020. How Do Visual Explanations Foster End Users' Appropriate Trust in Machine Learning?. In Proceedings of the 25th International Conference on Intelligent User Interfaces (IUI 2020), March 17-20, 2020, Cagliari, Italy, 189–201. New York, New York:Association for Computing Machinery (ACM).PNNL-SA-138276. doi:10.1145/3377325.3377480

Arendt D.L., N. Nur, Z. Huang, G. Fair, and W. Dou. 2020. Parallel Embeddings: a Visualization Technique for Contrasting Learned Representations. In Proceedings of the 25th International Conference on Intelligent User Interfaces (IUI 2020), March 17-20, 2020, Cagliari, Itally, 259–274. New York, New York:Association for Computing Machinery.PNNL-SA-148252. doi:10.1145/3377325.3377514

Thayer B.L., D.W. Engel, I. Chakraborty, K.P. Schneider, L. Ponder, and K. Fox. 2020. Improving End-Use Load Modeling Using Machine Learning and Smart Meter Data. In 53rd Hawaii International Conference on System Sciences (HICSS-53), January 6-10, 2020, Maui, Hawaii, 3055-3064. Honolulu, Hawaii:University of Hawaii.PNNL-SA-144442. doi:10.24251/HICSS.2020.373

Yin T., X. Zang, Z. Hou, P. Jacobson, R.P. Mueller, and Z. Deng. 2020. Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network. In Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS-53) January 6-10, 2020, Maui, HI, 932-939. Honolulu, Hawaii: University of Hawaii.PNNL-SA-144397. doi:10.24251/HICSS.2020.116

Reports

Raman A., S.M. Burrows, E. King, and L.M. Bramer. 2020. EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread? Richland, WA: Pacific Northwest National Laboratory.

Metz T.O., A.C. Sims, L.M. Bramer, and H.D. Mitchell. 2020. Multi-omics Characterization of the Host Response to COVID-19 Richland, WA: Pacific Northwest National Laboratory.

Jain M., K. Gupta, A. Visweswara Sathanur, V. Chandan, and M. Halappanavar. 2020. Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data Richland, WA: Pacific Northwest National Laboratory.

Browning N.D., and L. Kovarik. 2020. Optimizing Sub-Sampled STEM Imaging for Beam Sensitive Materials and Dynamic Processes Richland, WA: Pacific Northwest National Laboratory.

Reehl S.M., K.S. Kappagantula, E.J. Kautz, L.J. Gosink, and E.A. Machorro. 2020. Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations Richland, WA: Pacific Northwest National Laboratory.

Ma P., and P. Stinis. 2020. Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework Richland, WA: Pacific Northwest National Laboratory.

Cullen J.N., I. Pantoja Garcia, T.F. Grimes, and C.K. Simpson. 2020. Neural MUSE Analysis Richland, WA: Pacific Northwest National Laboratory.

Edgar T.W., W.J. Hofer, and M. Feghali. 2020. Model Driven Deception for Defense of Operational Technology Environments - CRADA 432 (Final Report) Richland, WA: Pacific Northwest National Laboratory.

Haupt S.E., L.K. Berg, A. Decastro, D.J. Gagne, P. Jimenez, T. Juliano, and B. Kosovic, et al. 2020. Outcomes of the DOE Workshop on Atmospheric Challenges for the Wind Energy Industry Richland, WA: Pacific Northwest National Laboratory.

Ren H., Z. Hou, H. Wang, and P.V. Etingov. 2020. Machine Learning for Synchrophasor Analysis Richland, WA: Pacific Northwest National Laboratory.

Gourisetti S.G., H. Reeve, J.A. Rotondo, and G.T. Richards. 2020. Facility Cybersecurity Framework Best Practices Richland, WA: Pacific Northwest National Laboratory.

Greg C .Allen, et al. 2020. “Understanding AI Technology.” Joint Artificial Intelligence Center (JAIC) Department of Defense.

McDermott T.E., J.D. Doty, J.G. O'Brien, C.R. Eppinger, and T. Becejac. 2020. Cybersecurity for Distance Relay Protection Richland, WA: Pacific Northwest National Laboratory.

2019

Journal articles

Rakshit A., P. Bandyopadhyay, J. Heindel, and S.S. Xantheas. 2019. Atlas of putative minima and low-lying energy networks of water clusters n = 3 - 25. Journal of Chemical Physics 151, no. 21:214307.PNNL-SA-147404. doi:10.1063/1.5128378

Fink G.A. 2019. Adversarial Artificial Intelligence: State of the Malpractice. Journal of Information Warfare 18, no. 4 (Special Edition):1-23.PNNL-SA-144984.

Alber M., A. Buganza Tepole, W.R. Cannon, S. De, S. Dura-Bernal, K. Garikipati, and G.E. Karniadakis, et al. 2019. Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences. npj Digital Medicine 2, no. 115.PNNL-SA-147139. doi:10.1038/s41746-019-0193-y

Stinis P., T.J. Hagge, A.M. Tartakovsky, and E.H. Yeung. 2019. Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. Journal of Computational Physics 397.PNNL-SA-133233. doi:10.1016/j.jcp.2019.07.042

Aksoy S.G., K.E. Nowak, E. Purvine, and S.J. Young. 2019. Relative Hausdorff distance for network analysis. Applied Network Science 4, no. 1:80.PNNL-SA-141621. doi:10.1007/s41109-019-0198-0

Li J., Y. Ma, X. Wu, A. Li, and K.J. Barker. 2019. PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite. CCF Transactions on High Performance Computing 1, no. 2:111-130.PNNL-SA-140675. doi:10.1007/s42514-019-00012-w

Sainju R., C. Ophus, M.B. Toloczko, D.J. Edwards, and Y. Zhu. 2019. Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel. Microscopy and Microanalysis 25, no. S2:1564-1565.PNNL-SA-146581. doi:10.1017/S1431927619008559

Li X., X. Fan, H. Ren, Z. Hou, Q. Huang, S. Wang, and O. Ciniglio. 2019. Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems. IEEE Transactions on Power Systems 34, no. 4:3297-3299.PNNL-SA-139624.doi:10.1109/TPWRS.2019.2912711

Liu Q., B. Liu, Y. Zhang, T. Hu, Z. Lin, G. Liu, and X. Wang, et al. 2019. Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: Options and mitigation strength in a global perspective. Global Change Biology 25, no. 6:2077-2093. PNNL-SA-127766. doi:10.1111/gcb.14613

McDermott J.E., J.R. Cort, E.S. Nakayasu, J.N. Pruneda, C.C. Overall, and J.N. Adkins. 2019. Prediction of Bacterial E3 Ubiquitin Ligase Effectors using Reduced Amino Acid Peptide Fingerprinting. PeerJ 7. PNNL-SA-138492. doi:10.7717/peerj.7055

Kautz E.J., A.R. Hagen, J.M. Johns, and D. Burkes. 2019. A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels. Computational Materials Science 161.PNNL-SA-138923. doi:10.1016/j.commatsci.2019.01.044

Conference papers

Mutlu B., G. Kestor, A. Cristal, O. Unsal, and S. Krishnamoorthy. 2019. Ground-Truth Prediction to Accelerate Soft-Error Impact Analysis for Iterative Methods. In IEEE 26TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS (HiPC 2019), December 17-20, 2019, Hyderabad, India, 333-344. Los Alamitos, California:IEEE Computer Society.PNNL-SA-148074. doi:10.1109/HiPC.2019.00048

Fu T., X. Lin, Z. Hou, and Z. Deng. 2019. Integrating Hybrid-Clustering and Localized Regression for Time Synchronization of a Hierarchical Underwater Acoustic Sensor Array. In OCEANS 2019 MTS/IEEE, October 27-31, 2019, Seattle, WA. Piscataway, New Jersey:IEEE.PNNL-SA-147446. doi:10.23919/OCEANS40490.2019.8962752

Zou P., A. Li, K.J. Barker, and R. Ge. 2019. Fingerprinting Anomalous Computation with RNN for GPU-accelerated HPC Machines. In IEEE International Symposium on Workload Characterization (IISWC 2019), November 3-5, 2019, Orlando, FL, 253-256. Piscataway, New Jersey:IEEE.PNNL-SA-144356. doi:10.1109/IISWC47752.2019.9042165

Puchko A.V., R.P. Link, B.J. Hutchinson, A.C. Snyder, and B.S. Kravitz. 2019. DeepClimGAN: A High-Resolution Climate Data Generator. In NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, December 14, 2019, Vancouver BC.PNNL-SA-147276.

Zhou H., Z. Hou, P.V. Etingov, and Y. Liu. 2019. Machine Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions. In The 3rd International Conference on Smart Grid and Smart Cities, June 25-28, 2019, Berkeley, CA, 85-91. Piscataway, New Jersey: IEEE.PNNL-SA-139398. doi:10.1109/ICSGSC.2019.00-13

Lin X., Z. Hou, H. Ren, and F. Pan. 2019. Approximate Mixed-Integer Programming Solution with Machine Learning Technique and Linear Programming Relaxation. In The 3rd International Conference on Smart Grid and Smart Cities, 101-107. Berkeley, California:IEEE.PNNL-SA-141126. doi:10.1109/ICSGSC.2019.00-11

Blaha L., C. Fallon, C. Gonzalez, and R.S. Gutzwiller. 2019. Opportunities and Challenges for Human-Machine Teaming in Cybersecurity Operations [Panel Discussion]. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, edited by C.L. Paul, 63, 442-446. Newbury Park, California: Sage.PNNL-SA-141762. doi:10.1177/1071181319631079

Zhou H., Z. Hou, P.V. Etingov, and Y. Liu. 2019. Machine-Learning-Based Investigation of the Associations between Residential Power Consumption and Weather Conditions. In The 3rd International Conference on Smart Grid and Smart Cities, (ICSGSC 2019), June 25-28, 2019, Berkeley, CA, 85-91. Piscataway, New Jersey:IEEE.PNNL-SA-144595. doi:10.1109/ICSGSC.2019.00-13

Saldanha E.G., B.L. Praggastis, T.V. Billow, and D.L. Arendt. 2019. ReLVis: Visual Analytics for Situational Awareness During Reinforcement Learning Experimentation. In 21st EG/VGTC Conference on Visualization, (EuroVis 2019), June 3-7, 2019, Porto, Portugal. Geneva: The Eurographics Association.PNNL-SA-138649. doi:10.2312/evs.20191168

Volkova S., E.M. Ayton, D.L. Arendt, Z. Huang, and B.J. Hutchinson. 2019. Explaining Multimodal Deceptive News Prediction Models. In Proceedings of the Thirteenth International AAAI Conference on Web and Social Media (ICWSM 2019), June 11-14, 2019, Munich, Germany, 659-662. Menlo Park, California: Association for the Advancement of Artificial Intelligence. PNNL-SA-135457.

Zhou H., Z. Hou, P.V. Etingov, and Y. Liu. 2019. Machine Learning of Commercial and Residential Load Components in Northwestern United States. In The ACM e-Energy 2019 Conference, 385-387. New York, New York: ACM. PNNL-SA-142105. doi:10.1145/3307772.3330160

Chakraborty I., V. Chandan, and D.L. Vrabie. 2019. A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation. In Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ, 508-515. New York, New York: ACM. PNNL-SA-141775. doi:10.1145/3307772.3331027

Rice T.R., G.E. Seppala, T.W. Edgar, D.M. Cain, and E.Y. Choi. 2019. Fused Sensor Analysis and Advanced Control of Industrial Field Devices for Security: Cymbiote Multi-Source Sensor Fusion Platform. In Proceedings of the Northwest Cybersecurity Symposium (NCS 2019), April 8-10, 2019, Richland, WA, Article No. 3. New York, New York:ACM.PNNL-SA-141222.doi:10.1145/3332448.3332455

Arendt D.L., E.G. Saldanha, R. Wesslen, S. Volkova, and W. Dou. 2019. Towards Rapid Interactive Machine Learning: Evaluating Tradeoffs of Classification without Representation. In International Conference on Intelligent User Interfaces, (IUI 2019), March 17-20, 2019, Marina del Ray, CA, 591-602. New York, New York:ACM.PNNL-SA-138765. doi:10.1145/3301275.3302280

Meng K., J. Li, G. Tan, and N. Sun. 2019. A Pattern Based Algorithmic Autotuner for Graph Processing on GPUs. In Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (PPoPP 2019), February 16-20, 2019, Washington, DC, 201-213. New York, New York:Association for Computing Machinery.PNNL-SA-140392. doi:10.1145/3293883.3295716

Castellana V.G., M. Minutoli, A. Tumeo, M. Lattuada, P. Fezzardi, and F. Ferrandi. 2019. Software Defined Architectures for Data Analytics. In Proceedings of the 24th Asia and South Pacific Design Automation Conference (ASPDAC 2019), January 21-24, 2019, Tokyo, Japan, 711-718. New York, New York:ACM.PNNL-SA-139669. doi:10.1145/3287624.3288754

Sun Y., X. Fan, Q. Huang, X. Li, R. Huang, T. Yin, and G. Lin. 2019. Local Feature Sufficiency Exploration for Predicting Security-constrained Generation Dispatch in Multi-Area Power Systems. In The 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), Orlando, FL, 1283-1289. Piscataway, New Jersey:IEEE.PNNL-SA-137906. doi:10.1109/ICMLA.2018.00208

Tipireddy R., and A.M. Tartakovsky. 2019. Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids. In Proceedings of the 52nd Hawaii International Conference on System Sciences (HICCS 2019), January 8-11, 2019, Honolulu, HI, 3438-3444. Honolulu, Hawaii: University of Hawaii.PNNL-SA-135858. doi:10125/59779

Reports

Chin G., J.M. Brandi-Lozano, W.I. Gustafson, K. Porterfield, and L. Riihimaki. 2019. Data Assessment and Assimilation for Atmospheric Radiation Measurement Data Using Dynamic Bayesian Networks Richland, WA: Pacific Northwest National Laboratory.

Ahmed O., R.A. Buractaon, M.E. Alam, A. Hossain, and M.B. Alam. 2019.Vehicle Exhaust Monitoring Applications using loT platform Richland, WA: Pacific Northwest National Laboratory.

Carado A.J., G.C. Eiden, J.D. Ward, R.J. Jasper, and A.J. Carman. 2019. Enhanced Ion Detection for Atomic Mass Spectrometry Richland, WA: Pacific Northwest National Laboratory.

Coleman A., J.D. Tagestad, M.J. Henry, J.P. Almquist, J.J. Harrison, J.D. Hendry, and I.R. Herrera, et al. 2019. Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response Richland, WA: Pacific Northwest National Laboratory.

Stanfill B.A., L.M. Bramer, T. Liu, and S.M. Akers. 2019. Machine Learning for Rapid Biomarker Discovery via Image-Omic Fusion Richland, WA: Pacific Northwest National Laboratory.

Bao J., C. Wang, Z. Xu, and B.J. Koeppel. 2019. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization Richland, WA: Pacific Northwest National Laboratory.

Ramuhalli P., N.C. Anheier, C.A. Barrett, E.J. Berglin, D.V. Colameco, K.M. Denslow, and C.W. Enderlin, et al. 2019. Final Report on the Viability of Acoustic Techniques for Density and Mass Flow in Enrichment Plants Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Zhu Y., G.W. Roberts, R. Sainju, B.J. Hutchinson, R.J. Kurtz, M.B. Toloczko, and D.J. Edwards, et al. 2019. ADVANCED-STEM-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION OF DEFECTS IN STEELS. In Fusion Materials Semiannual Progress Report for the Period Ending December 31, 2018, edited by F.W. Wiffen and S. Melton. 83-84. DOE-ER-0313/65. Oak Ridge, Tennessee: Oak Ridge National Laboratory.PNNL-SA-141280.

2018

Journal articles

Gao M., H. Li, D. Liu, J. Tang, X. Chen, X. Chen, and G. Bloschl, et al. 2018. Identifying the Dominant Controls on Macropore Flow Velocity in Soils: A Meta-analysis. Journal of Hydrology 567.PNNL-SA-139024. doi:10.1016/j.jhydrol.2018.10.044

Liu R., R.J. Rallo Moya, and Y. Cohen. 2018. Fractal Dimension Calculation for Big Data Using Box Locality Index. Annals of Data Science 5, no. 4:549–563.PNNL-SA-127675. doi:10.1007/s40745-018-0152-5

Chen M., and D.A. Dixon. 2018. Machine-Learning Approach for the Development of Structure-Energy Relationships of ZnO Nanoparticles. Journal of Physical Chemistry C 122, no. 32:18621-18639.PNNL-SA-151902. doi:10.1021/acs.jpcc.8b01667

Rauchenstein L.T., A. Vishnu, X. Li, and Z. Deng. 2018. Improving Underwater Localization Accuracy with Machine Learning. Review of Scientific Instruments 89, no. 7:074902.PNNL-SA-120624. doi:10.1063/1.5012687

Inanlouganji A., A. Reddy, and S. Katipamula. 2018. Evaluation of regression and neural network models for solar forecasting over different short-term horizons. Science and Technology for the Built Environment 24, no. 9:1004-1013.PNNL-SA-140536. doi:10.1080/23744731.2018.1464348

Conference papers

Haddal R., N.K. Hayden, and S.L. Frazar. 2018. Autonomous systems, artificial intelligence and safeguards. In IAEA Symposium on International Safeguards: Building Future Safeguards Capabilities, November 5-8, 2018, Vienna, Austria. Vienna: IAEA. PNNL-SA-137130.

Chakraborty I., S. Nandanoori, and S. Kundu. 2018. Virtual Battery Parameter Identification using Transfer Learning based Stacked Autoencoder. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), December 17-20, 2018, Orlando, Florida. Piscataway, New Jersey:IEEE.PNNL-SA-137721. doi:10.1109/ICMLA.2018.00206

Sakloth K., W. Beckner, J. Pfaendtner, and G.B. Goh. 2018. IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks. In Proceedings of the IEEE International Conference on Big Data,(Big Data 2018), December 10-13, 2018, Seattle, WA, edited by Y. Song, et al, 1465-1473, Article No. 8622512. Piscataway, New Jersey:IEEE.PNNL-SA-138709. doi:10.1109/BigData.2018.8622512

Johns J.M., and K.J. Geelhood. 2018. OPTIMIZATION OF FAST FISSION GAS RELEASE MODEL PARAMETERS USING MACHINE LEARNING ACCELERATED EVOLUTIONARY ALGORITHMS. In TopFuel 2018: Reactor Fuel Performance, September 30-October 4, 2018, Prague, Czech Republic.PNNL-SA-137821.

Fink G.A., and Y.I. Shulga. 2018. Helping IT and OT Defenders Collaborate. In Proceedings of the IEEE International Conference on Industrial Internet (ICII 2018), October 21-23, 2018, Seattle, WA, 188-194; Paper No. 8539125. Piscataway, New Jersey:IEEE.PNNL-SA-138585. doi:10.1109/ICII.2018.00036

Ren H., Z. Hou, and P.V. Etingov. 2018. Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), June 24-28, 2018, Boise, ID, 1-5. Piscataway, New Jersey: IEEE.PNNL-SA-131920. doi:10.1109/PMAPS.2018.8440495

Hou Z., J.D. Follum, P.V. Etingov, F.K. Tuffner, D. Kosterev, and G.H. Matthews. 2018. Machine Learning of Factors Influencing Damping and Frequency of Dominant Inter-area Modes in the WECC Interconnect. In IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2018), June 24-28, 2018, Boise, ID, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-130439. doi:10.1109/PMAPS.2018.8440361

Glenski M., T. Weninger, and S. Volkova. 2018. "Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources." In 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), July 15-20, 2018, Melbourne, Australia, 2, 176-181. Stroudsburg, Pennsylvania: Association for Computational Linguistics. PNNL-SA-133420.

Fallon C., and L.M. Blaha. 2018. Improving Automation Transparency: Addressing Some of Machine Learning’s Unique Challenges. In Proceedings of the 12th International Conference on Augmented Cognition (AC 2018), July 15-20, 2018, Las Vegas, NV. Lecture Notes in Computer Science, 10915, 245-254. Cham:Springer.PNNL-SA-132786. doi:10.1007/978-3-319-91470-1_21

Kestor G.G., I.B. Peng, R. Gioiosa, and S. Krishnamoorthy. 2018. Understanding scale-dependent soft-error behavior of scientific applications. In Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2018), May 1-4, 2018, Washington DC, 482-491. Piscataway, New Jersey:IEEE.PNNL-SA-132744. doi:10.1109/CCGRID.2018.00075

Brown A., B.J. Hutchinson, A.R. Tuor, and N.M. Nichols. 2018. Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. In Proceedings of the 1st Workshop on Machine Learning for Computing Systems (MLCS 2018), June 12, 2018, Tempe, AZ, Paper No. 1. New York, New York:ACM.PNNL-SA-133000. doi:10.1145/3217871.3217872

Volkova S., S.M. Ranshous, and L.A. Phillips. 2018. "Predicting Foreign Language Usage from English-Only Social Media Posts." In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018), June 1-6, 2018, New Orleans, Louisiana, 608-614. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-124151.

Olney R., A.R. Tuor, F. Jagodzinski, and B.J. Hutchinson. 2018. Protein Mutation Stability Ternary Classification using Neural Networks and Rigidity Analysis. In 10th International Conference on Bioinformatics and Computational Biology (BICOB 2018), March 19-21, 2018, Las Vegas, NV, edited by A.M. Al-Mubaid, O. Eulenstein and Q. Ding. Winona, Minnesota: The International Society for Computers and Their Applications (ISCA). PNNL-SA-132051.

Kestor G.G., B. Mutlu, J.B. Manzano Franco, O. Subasi, O. Unsal, and S. Krishnamoorthy. 2018. Comparative Analysis of Soft-Error Detection Strategies: A Case Study with Iterative Methods. In Proceedings of the 15th ACM International Conference on Computing Frontiers (CF 2018), May 8-10, 2019, Ishia, Italy, 173-182. New York, New York: ACM.PNNL-SA-133097. doi:10.1145/3203217.3203240

Goh G.B., C.M. Siegel, A. Vishnu, N.O. Hodas, and N.A. Baker. 2018. How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?. In IEEE Winter Conference on Applications of Computer Vision (WACV 2018), March 12-15, 2018, Lake Tahoe, NV, 1340-1349. Piscataway, New Jersey: IEEE.PNNL-SA-127201. doi:10.1109/WACV.2018.00151

Volkova S., and J. Jang. 2018. Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media. In The World Wide Web Conference (WWW 2018): Journalism, Misinformation and Fact Checking, April 23-27, 2018, Lyon, France, 575-583. Geneva: International World Wide Web Conferences Steering Committee.PNNL-SA-132310. doi:10.1145/3184558.3188728

Wang L., J. Ye, Y. Zhao, W. Wu, A. Li, S. Song, and Z. Xu, et al. 2018. SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, (PPOPP 2018), February 24-28, 2018, Vienna, Austria, 41-53. New York, New York: ACM.PNNL-SA-143407. doi:10.1145/3200691.3178491

Tuor A.R., S.P. Kaplan, B.J. Hutchinson, N.M. Nichols, and S.M. Robinson. 2018. Deep Learning for Unsupervised Insider Threat Detection in Structured Cyber Security Data Streams. In Artificial Intelligence for Cyber Security Workshop (AAAI-2017), February 4-5, 2017, San Francisco, CA, 224-231. Palo Alto, California: AAAI Press.PNNL-SA-122883.

Arendt D.L., E.A. Grace, and S. Volkova. 2018. Interactive Machine Learning at Scale with CHISSL. In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-2018), February 2-7, 2018, New Orleans, Louisiana, 8194-8195. Palo Alto, California: Association for the Advancement of Artificial Intelligence. PNNL-SA-129748.

Tuor A.R., R. Baerwolf, N. Knowles, B.J. Hutchinson, N.M. Nichols, and R.J. Jasper. 2018. Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, Workshop for Artificial Intelligence for Cyber Security (AICS 2018), February 2-7, 2018, New Orleans, LA, Paper No. arXiv:1712.00557. Palo Alto, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-130482.

Rubio-Herrero J., V. Chandan, C.M. Siegel, A. Vishnu, and D.L. Vrabie. 2018. A Learning Framework for Control-Oriented Modeling of Buildings. In IEEE International Conference on Machine Learning and Applications (ICMLA 2017) December 18-21, 2017, Cancun, Mexico. Piscataway, New Jersey: IEEE. PNNL-SA-128813. doi:10.1109/ICMLA.2017.00079

Reports

Andersen E.S., B.G. Amidan, J.S. Banning, and A. Silverstein. 2018. PNNL Plan for Acquiring, Anonymizing, and Protecting Utility Data Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Charles L.E., W.P. Smith, J. Rounds, and J.A. Mendoza. 2018. Text-based Analytics for Biosurveillance. In Advanced Data Analytics in Health, edited by PJ Giabbanelli, VK Mago and EI Papageorgiou. 117-131. Cham:Springer International Publishing.PNNL-SA-126938. doi:10.1007/978-3-319-77911-9

2017

Journal articles

Volkova S., E.M. Ayton, K. Porterfield, and C.D. Corley. 2017. Forecasting Influenza-like Illness Dynamics for Military Populations using Neural Networks and Social Media. PLoS One 12, no. 12:e0188941.PNNL-SA-124148. doi:10.1371/journal.pone.0188941

Crouser J., L. Franklin, and K.A. Cook. 2017. Rethinking Visual Analytics for Streaming Data Applications. IEEE Internet Computing 21, no. 4:72-76.PNNL-SA-125092. doi:10.1109/MIC.2017.2911428

Hao W., A.J. Stevens, H. Yang, M. Gehm, and N.D. Browning. 2017. Compressive Classification for TEM-EELS. Microscopy and Microanalysis 23, no. S1:108-109.PNNL-SA-127626. doi:10.1017/S1431927617001222

Panyala A.R., D.G. Chavarria, J.B. Manzano Franco, A. Tumeo, and M. Halappanavar. 2017. Exploring Performance and Energy Tradeoffs for Irregular Applications: A Case Study on the Tilera Many-core Architecture. Journal of Parallel and Distributed Computing 104.PNNL-SA-118976.doi:10.1016/j.jpdc.2016.06.006

Goh G.B., N.O. Hodas, and A. Vishnu. 2017. Deep Learning for Computational Chemistry. Journal of Computational Chemistry 38, no. 16:1291-1307.PNNL-SA-121040.doi:10.1002/jcc.24764

Conference papers

Tumeo A. 2017. Architecture Independent Integrated Early Performance and Energy Estimation. In Eighth International Green and Sustainable Computing Conference (IGSC 2017), October 23-25, 2017, Orlando, FL, 1-6. Piscataway, New Jersey:IEEE.PNNL-SA-129380. doi:10.1109/IGCC.2017.8323602

Goh G.B., C.M. Siegel, A. Vishnu, and N.O. Hodas. 2017. ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction. In Machine Learning for Molecules and Materials (NIPS 2017 Workshop), December 8, 2017, Long Beach, California. La Jolla, California: Neural Information Processing Systems Foundation, Inc.PNNL-SA-129942.

Franklin L., M.A. Pirrung, L.M. Blaha, M.V. Dowling, and M. Feng. 2017. Toward a Visualization-Supported Workflow for Cyber Alert Management using Threat Models and Human-Centered Design. In IEEE Symposium on Visualization for Cyber Security (VizSec 2017), October 2, 2017, Phoenix, Arizona. Piscataway, New Jersey: IEEE.PNNL-SA-127976.doi:10.1109/VIZSEC.2017.8062200

Krause J., A. Dasgupta, J. Swartz, Y. Aphinyanaphongs, and E. Bertini. 2017. A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations. In IEEE Conference on Visual Analytics Science and Technology (VAST 2017), October 3-6, 2017, Phoenix, AZ, 162-172. Piscataway, New Jersey:IEEE.PNNL-SA-125049. doi:10.1109/VAST.2017.8585720

Amatya V.C., A. Vishnu, C.M. Siegel, and J.A. Daily. 2017. What does fault tolerant Deep Learning need from MPI?. In Proceedings of the 24th European MPI Users' Group Meeting, September 25-28, 2017, Chicago, Illinois, Paper No. 13. New York, New York:ACM.PNNL-SA-127971. doi:10.1145/3127024.3127037

Subasi O., S. Di, P. Balaprakash, O. Unsal, J. Labarta, A. Cristal, and S. Krishnamoorthy, et al. 2017. MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection. In IEEE International Conference on Cluster Computing (CLUSTER 2017), September 5-8, 2017, Honolulu, HI, 717-724. Los Alamitos, California: IEEE Computer Society.PNNL-SA-128115. doi:10.1109/CLUSTER.2017.128

Phillips L.A., K.J. Shaffer, D.L. Arendt, N.O. Hodas, and S. Volkova. 2017. "Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams." In Proceedings of the 2nd Workshop on Representation Learning for NL (Rep4NLP@ACL 2017), August 3, 2017, Vancouver Canada, 201-210. Stroudsburg, Pennsylvania: Association for Computational Linguistic.PNNL-SA-122225.

Volkova S., and E.B. Bell. 2017. Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages. In Proceedings of the 11th International AAAI Conference on Web and Social Media (ICWSM 2017), May 15-18, 2017, Montreal, Canada, 290-298. Menlo Park, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-116993.

Jasper R.J., and L.M. Blaha. 2017. Interface Metaphors for Interactive Machine Learning. In 11th International Conference on Augmented Cognition: Augmented Cognition. Neurocognition and Machine Learning (AC 2017) July 9-14, 2017, Vancouver, BC, Canada. Lecture Notes in Computer Science, edited by DD Schmorrow and CM Fidopiastis, 10284, 521-534. Cham:Springer.PNNL-SA-124439. doi:10.1007/978-3-319-58628-1_39

Arendt D.L., C. Komurlu, and L.M. Blaha. 2017. CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning. In 11th International Conference on Augmented Cognition: Augmented Cognition. Neurocognition and Machine Learning (AC2017), July 9-14, 2017, Vancouver, BC, Canada. Lecture Notes in Computer Science, edited by DD Schmorrow and CM Fidopiastis, 10284, 429-448. Cham:Springer.PNNL-SA-124302. doi:10.1007/978-3-319-58628-1_33

Stewart I.B., D.L. Arendt, E.B. Bell, and S. Volkova. 2017. Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network. In THE 11TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM-17), May 16-18, 2017, Montreal, Canada, 672-675. Palo Alto, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-124572.

Tamagnini P., J.W. Krause, A. Dasgupta, and E. Bertini. 2017. Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations. In Proceedings of the 2nd Workshop on Human-in-the-Loop Data Analytics (HILDA 2017) May 14-19, 2017, Chicago, Illinois, Article No. 6. New York, New York:ACM.PNNL-SA-124676. doi:10.1145/3077257.3077260

Stevens A.J., Y. Pu, Y. Sun, G. Spell, and L. Carin. 2017. Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), April 20-22, 2017, Fort Lauderdale, Florida, 54, 121-129. Cambridge: Proceedings of Machine Learning Research Press.PNNL-SA-121675.

Pathak N., M.J. Henry, and S. Volkova. 2017. Understanding Social Media’s Take on Climate Change through Large-Scale Analysis of Targeted Opinions and Emotions. In The AAAI 2017 Spring Symposium on Artificial Intelligence for Social Good (AISOC 2017), March 27-29, 2017, Stanford, California, 45-52. Palo Alto, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-122223.

Siegel C.M., J.A. Daily, and A. Vishnu. 2017. Adaptive Neuron Apoptosis for Accelerating Deep Learning on Large Scale Systems. In IEEE International Conference on Big Data (Big Data 2016), December 5-8, 2016, Washington DC, 753-762. Piscataway, New Jersey: IEEE.PNNL-SA-120738. doi:10.1109/BigData.2016.7840668

Tuor A.R., S.P. Kaplan, B.J. Hutchinson, N.M. Nichols, and S.M. Robinson. 2017. Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. In The AAAI Workshop on Artificial Intelligence for Cyber Security, 224-231; WS-17-04. Palo Alto, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-122088.

Book chapters

Mylrea M.E., and S.G. Gourisetti. 2017. Cybersecurity and Optimization in Smart “Autonomous” Buildings. In Autonomy and Artificial Intelligence: A Threat or Savior?, edited by W Lawless, et al. 263-294. Cham:Springer.PNNL-SA-127260. doi:10.1007/978-3-319-59719-5_12

Cramer N.O., G.C. Nakamura, and A. Endert. 2017. The Impact of Streaming Data on Sensemaking with Mixed-Initative Visual Analytics. In Augmented Cognition. Neurocognition and Machine Learning. AC 2017. Lecture Notes in Computer Science, edited by D. Schmorrow and C. Fidopiastis. 478-498. Cham:Springer.PNNL-SA-124300. doi:10.1007/978-3-319-58628-1_36

Chikkagoudar S., S. Chatterjee, D.G. Thomas, T.E. Carroll, and G. Muller. 2017. Machine Learning. In Research Methods for Cyber Security, edited by TW Edgar and DO Manz. 153-173. Cambridge, Massachusetts: Syngress.PNNL-SA-122747. doi:10.1016/B978-0-12-805349-2.00006-6

2016

Journal articles

Garimella S., T.B. Kristensen, K. Ignatius, A. Welti, J. Voigtlander, G.R. Kulkarni, and F. Sagan, et al. 2016. The SPectrometer for Ice Nuclei (SPIN): an instrument to investigate ice nucleation. Atmospheric Measurement Techniques 9, no. 7:2781-2795.PNNL-SA-119306. doi:10.5194/amt-9-2781-2016

Pu Y., X. Yuan, A.J. Stevens, C. Li, and L. Carin. 2016. A Deep Generative Deconvolutional Image Model. Journal of Machine Learning Research 51. PNNL-SA-115998.

Conference papers

Webb-Robertson B.M., L.M. Bramer, S.M. Reehl, T.O. Metz, Q. Zhang, M. Rewers, and B. Frohnert. 2016. ROFI – The use of Repeated Optimization for Feature Interpretation. In International Conference on Computational Science and Computational Intelligence (CSCI 2016), December 15-17, 206, Las Vegas, Nevada, edited by HR Arabnia, L Deligiannidis and M Yang. Piscataway, New Jersey:IEEE. PNWD-SA-10276. doi:10.1109/CSCI.2016.0013

Krause J., A. Dasgupta, J. Fekete, and E. Bertini. 2016. SeekAView: An Intelligent Dimensionality Reduction Strategy for Navigating High-Dimensional Data Spaces. In IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV 2016), October 23-28, 2016, Baltimore, Maryland, edited by M Hadwiger, R Maciejewski and K Moreland, 11-19. Piscataway, New Jersey: IEEE.PNNL-SA-120844. doi:10.1109/LDAV.2016.7874305

Tallent N.R., K.J. Barker, R. Gioiosa, A. Marquez, G. Kestor, S. Song, and A. Tumeo, et al. 2016. Assessing Advanced Technology in CENATE. In Proceedings of the IEEE International Conference on Networking, Architecture, and Storage (NAS 2016), August 8-10, 2016, Long Beach, California. Piscataway, New Jersey: IEEE.PNNL-SA-119257.doi:10.1109/NAS.2016.7549392

Han K., K.A. Cook, and P.C. Shih. 2016. Exploring Effective Decision Making through Human-Centered and Computational Intelligence Methods. In Human Centred Machine Learning at CHI 2016, May 7-12, 2016, San Jose, California. New York, New York: ACM. PNNL-SA-116344.

Landwehr J.B., J.D. Suetterlein, A. Marquez, J.B. Manzano Franco, and G.R. Gao. 2016. Application Characterization at Scale: Lessons learned from developing a distributed Open Community Runtime system for High Performance Computing. In Proceedings of the ACM International Conference on Computing Frontiers (CF 2016), May 16-28, 2016, Como, Italy. New York, New York: ACM.PNNL-SA-116663. doi:10.1145/2903150.2903166

Jurrus E.R., N.O. Hodas, N.A. Baker, T.P. Marrinan, and M.D. Hoover. 2016. Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis. In 2016 IEEE International Symposium on Technologies for Homeland Security, May 10-11, 2016, Waltham, MA. Piscataway, New Jersey:IEEE.PNNL-SA-114724. doi:10.1109/THS.2016.7568959

Potash P.J., E.B. Bell, and J.J. Harrison. 2016. Using Topic Modeling and Text Embeddings to Predict Deleted Tweets. In AAAI Workshop on Incentive and Trust in E-Communities (WIT-EC'16), February 12–17 2016, Phoenix, Arizona. Palo Alto, California: Association for the Advancement of Artificial Intelligence. PNNL-SA-114673.

Pavalanathan U., V.V. Datla, S. Volkova, L.E. Charles-Smith, M.A. Pirrung, J.J. Harrison, and A.R. Chappell, et al. 2016. Discourse, Health and Well-being of Military Populations through the Social Media Lens. In The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, 796-803. Palo Alto, California: Association for the Advancement of Artificial Intelligence.PNNL-SA-114305.

2015

Journal articles

Schneck K., B. Cabrera, D.G. Cerdeno, D.G. Cerdeno, V. Mandic, H.E. Rogers, and R. Agnese, et al. 2015. Dark matter effective field theory scattering in direct detection experiments. Physical Review D 91, no. 9:Article No. 092004.PNNL-24214. doi:10.1103/PhysRevD.91.092004

You Y., H. Fu, S. Song, A. Randles, D.J. Kerbyson, A. Marquez, and G. Yang, et al. 2015. Scaling Support Vector Machines On Modern HPC Platforms. Journal of Parallel and Distributed Computing 76.PNNL-SA-105673. doi:10.1016/j.jpdc.2014.09.005

Conference papers

Yin J., and D. Zhao. 2015. Data Confidentiality Challenges in Big Data Applications. In IEEE International Conference on Big Data (Big Data), October 29-November 1, 2015, Santa Clara, California, 2886-2888. Piscataway, New Jersey:IEEE.PNNL-SA-114675. doi:10.1109/BigData.2015.7364111

Bramer L.M., S. Chatterjee, A.E. Holmes, S.M. Robinson, S.F. Bradley, and B.M. Webb-Robertson. 2015. A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. In The 11th International Conference on Data Mining (DMIN 2015), July 27-30, 2015, Las Vegas, Nevada, 162-167. Athens, Georgia: CSREA Press.PNNL-SA-110014.

Vishnu A., J. Narasimhan, L. Holder, D.J. Kerbyson, and A. Hoisie. 2015. Fast and Accurate Support Vector Machines on Large Scale Systems. In IEEE International Conference on Cluster Computing (CLUSTER 2015), September 8-11, 2015, Chicago, Illinois, 110-119. Piscataway, New Jersey: IEEE.PNNL-SA-110940.doi:10.1109/CLUSTER.2015.26

Lin J., K. Hamidouche, J. Zheng, X. Lu, A. Vishnu, and D. Panda. 2015. Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM. In OpenSHMEM 2015: Second Workshop on OpenSHMEM and Related Technologies, August 4-6, 2015, Annapolis, Maryland, edited by G Venkata, et al, 164-177. Berlin: Springer.PNNL-SA-113024. doi:10.1007/978-3-319-26428-8

Chavarría-Miranda D., A.R. Panyala, M. Halappanavar, J.B. Manzano Franco, and A. Tumeo. 2015. Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture. In Proceedings of the 12th ACM International Conference on Computing Frontiers (CF 2015), May 18-21, 2015, Ischia, Italy, Article No. 12. New York, New York: ACM.PNNL-SA-108596. doi:10.1145/2742854.2742865

Antoniak M.A., E.B. Bell, and F. Xia. 2015. Leveraging Paraphrase Labels to Extract Synonyms from Twitter. In Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS-28), May 18-20, 2015, Hollywood, Florida, 3-7. Palo Alto, California: AAAI Press. PNNL-SA-106823.

2014

Journal articles

Brown E.T., A. Ottley, H. Zhao, Q. Lin, R. Souvenir, A. Endert, and R. Chang. 2014. Finding Waldo: Learning about Users from their Interactions. IEEE Transactions on Visualization and Computer Graphics 20, no. 12:1663-1672.PNNL-SA-101815. doi:10.1109/TVCG.2014.2346575

Gil Y., M.T. Greaves, J. Hendler, and H. Hirsch. 2014. Amplify scientific discovery with artificial intelligence. Science 346, no. 6206:171-2.PNNL-SA-105817. doi:10.1126/science.1259439

Bond-Lamberty B., A. Rocha, K.V. Calvin, B. Holmes, C. Wang, and M.L. Goulden. 2014. Disturbance legacies and climate jointly drive tree growth and mortality in an intensively studied boreal forest. Global Change Biology 20, no. 1:216-227.PNNL-SA-95459.doi:10.1111/gcb.12404

Conference papers

Endert A., C. North, R. Chang, and M. Zhou. 2014. Toward Usable Interactive Analytics: Coupling Cognition and Computation. In KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA 2014), August 24, 2016, New York, New York, edited by P Chau, et al, 52-56. Atlanta, Georgia: Georgia Institute of Technology. PNNL-SA-103744.

Ray A., L. Holder, and S. Choudhury. 2014. Frequent Subgraph Discovery in Large Attributed Streaming Graphs. In Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BIGMINE 2014), August 24, 2014, 36, 166-181. New York: Journal of Machine Learning Research.PNNL-SA-103377.

Webster J.B., L.E. Erikson, C.M. Toomey, and V.A. Lewis. 2014. PNNL Strategic Goods Testbed: A Data Library for Illicit Nuclear Trafficking. In Information Analysis Technologies, Techniques and Methods for Safeguards, Nonproliferation and Arms Control Verification Workshop, May 12-14, 2014, Portland, Oregon, 168-172. Deerfield, Illinois: Institute of Nuclear Materials Management (INMM).PNNL-SA-102611.

Book chapters

Tasdizen T., M. Seyedhosseini, T. Liu, C. Jones, and E.R. Jurrus. 2014. Image Segmentation for Connectomics Using Machine Learning. In Computational Intelligence in Biomedical Imaging, edited by K Suzuki. 237-278. New York, New York:Springer.PNNL-SA-105561. doi:10.1007/978-1-4614-7245-2_10

2013

Journal articles

Bright I., G. Lin, and N. Kutz. 2013. Compressive Sensing Based Machine Learning Strategy For Characterizing The Flow Around A Cylinder With Limited Pressure Measurements. Physics of Fluids 25, no. 12:Article No. 127102.PNNL-SA-95027.doi:10.1063/1.4836815

Jurrus E.R., S. Watanabe, R.J. Giuly, A.R. Paiva, M.H. Ellisman, E.M. Jorgensen, and T. Tasdizen. 2013. Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images. Neuroinformatics 11, no. 1:5-29.PNNL-SA-87451. doi:10.1007/s12021-012-9149-y

Conference papers

Hafen R.P., L.J. Gosink, J.E. McDermott, K.D. Rodland, K. Kleese-Van Dam, and W.S. Cleveland. 2013. Trelliscope: A System for Detailed Visualization in Analysis of Large Complex Data. In IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV 2013), October 13-14, 2013, Atlanta, Georgia, 105-112. Piscataway, New Jersey: IEEE.PNNL-SA-95831. doi:10.1109/LDAV.2013.6675164

Song S., N.R. Tallent, and A. Vishnu. 2013. Exploring Machine Learning Techniques For Dynamic Modeling on Future Exascale Systems. In Modeling & Simulation of Exascale Systems & Applications: Workshop on Modeling & Simulation of Exascale Systems & Applications, September 18-19, 2013, Seattle, Washington. Washington DC: US Department of Energy, Office of Advanced Scientific Computing Research. PNNL-SA-105672.

Hafen R.P., and T.J. Critchlow. 2013. EDA and ML - A Perfect Pair for Large-Scale Data Analysis. In IEEE 27th Parallel and Distributed Processing Symposium Workshop & PhD Forum(IPDPSW 2013), May 20-24, 2013, Cambridge, MA, 1894-1898. Piscataway, New Jersey: IEEE.PNNL-SA-93734. doi:10.1109/IPDPSW.2013.118

Stevens A.J., Y. Sun, X. Song, G. Parker, and G. Parker. 2013. Model Identification for Optimal Diesel Emissions Control. In Proceedings of the 30th International Conference on Machine Learning (ICML), June 16-21 2013, Atlanta, Georgia, edited by S Dasgupta and D McAllester, 28. Madison, Wisconsin: Omnipress.PNNL-SA-94339.

Dillard S.E., M.J. Henry, S.J. Bohn, and L.J. Gosink. 2013. Coherent Image Layout using an Adaptive Visual Vocabulary. In Image Processing: Machine Vision Applications VI, February 3, 2013, Burlingame, California. Proceedings of the SPIE, edited by PR Bingham, EY Lam, 8661, Paper No. 86610Q. Burlingame, California: SPIE.PNNL-SA-92482. doi:10.1117/12.2004733

Reports

Gastelum Z.N., R.R. LaMothe, J.L. Barr, and M.J. Henry. 2013. Precision Information Environment (PIE) for International Nuclear Safeguards: Use Case and Technical Requirements Richland, WA: Pacific Northwest National Laboratory.

2012

Journal articles

Bond-Lamberty B., A.G. Bunn, and A.M. Thomson. 2012. Multi-Year Lags between Forest Browning and Soil Respiration at High Northern Latitudes. PLoS One 7, no. 11:Article No. e50441.PNNL-SA-78393. doi:10.1371/journal.pone.0050441

Kangas L.J., T.O. Metz, G. Isaac, B.T. Schrom, B. Ginovska-Pangovska, L. Wang, and L. Tan, et al. 2012. In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids. Bioinformatics 28, no. 13:1705-1713.PNNL-SA-85694. doi:10.1093/bioinformatics/bts194

Conference papers

Carroll T.E., D.O. Manz, T.W. Edgar, and F.L. Greitzer. 2012. Realizing Scientific Methods for Cyber Security. In LASER '12: Proceedings of the 2012 Workshop on Learning from Authoritative Security Experiment Results, July 18-19, 2012, Arlington, Virginia, 19-24. New York: Association for Computing Machinery.PNNL-SA-87207. doi:10.1145/2379616.2379619

Scherrer C., M. Halappanavar, A. Tewari, and D.J. Haglin. 2012. Scaling Up Coordinate Descent Algorithms for Large l1 Regularization Problems. In Proceedings of the 29th International Conference on Machine Learning (ICML 2012), June 26, 2012, Edinburgh, Scotland, edited by J Langford and J Pineau. Madison, Wisconsin: International Machine Learning Society.PNNL-SA-87037.

Barber C.A., and C.S. Oehmen. 2012. An Efficient Machine Learning Approach To Low-Complexity Filtering In Biological Sequences. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), May9-12, 2012, San Diego, CA, 237-243. Piscataway, New Jersey:IEEE.PNNL-SA-84473. doi:10.1109/CIBCB.2012.6217236

Bell E.B., E.J. Marshall, R.E. Hull, A.K. Fligg, A.P. Sanfilippo, D.S. Daly, and D.W. Engel. 2012. Classifying Scientific Performance on a Metric-by-Metric Basis. In Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 23-25, 2012, Marco Island, Florida, edited by GM Youndblood and PM McCarthy, 400-403. Palo Alto, California: AAAI Press. PNWD-SA-9767.

Best D.M., J.R. Bruce, S.T. Dowson, O.J. Love, and L.R. McGrath. 2012. Web-Based Visual Analytics for Social Media. In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, June 4-6, 2012, Dublin, Ireland. Palo Alto, California: Association for the Advancement of Artificial Intelligence. PNNL-SA-86200.

Reports

Keller P.E. 2012. Categorization of Radioxenon Richland, WA: Pacific Northwest National Laboratory.

Book chapters

van Dam H.J. 2012. Parallel Quantum Chemistry at the Cross Roads. In Handbook of Research on Computational Science and Engineering: Theory and Practice, edited by J Leng and WW Sharrock. 239-266. Hershey, Pennsylvania:IGI Global.PNNL-SA-73070.

2011

Journal articles

Sanfilippo A.P., L.R. McGrath, and P.D. Whitney. 2011. VIOLENT FRAMES IN ACTION. Dynamics of Asymmetric Conflict 4, no. 2:103-112.PNNL-SA-77551.

Adams L.G., S. Khare, S.D. Lawhon, C.A. Rossetti, H.A. Lewin, M.S. Lipton, and J.E. Turse, et al. 2011. Enhancing the role of veterinary vaccines reducing zoonotic diseases of humans: Linking systems biology with vaccine development. Vaccine 29, no. 41:7197-7206.PNNL-SA-84155.doi:10.1016/j.vaccine.2011.05.080

McDermott J.E., M.N. Archuleta, B.D. Thrall, J.N. Adkins, and K.M. Waters. 2011. Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation. PLoS One 6(2). PNWD-SA-9011. doi:10.1371/journal.pone.0014673

Webb-Robertson B.M., A.L. Bunn, and V.L. Bailey. 2011. Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning. Journal of Environmental Radioactivity 102, no. 1:67-71.PNNL-SA-72816. doi:10.1016/j.jenvrad.2010.09.005

Conference papers

Adolf R.D., D.J. Haglin, M. Halappanavar, Y. Chen, and Z. Huang. 2011. Techniques for Improving Filters in Power Grid Contingency Analysis. In Proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM), August 30-September 3, 2011, New York. Lecture Notes in Computer Science, edited by P Perner, 6871, 599-611. Berlin:Springer-Verlag.PNNL-SA-77563. doi:10.1007/978-3-642-23199-5_44

Schrom B.T., L.J. Kangas, B. Ginovska, T.O. Metz, and J.H. Miller. 2011. Charge Prediction of Lipid Fragments in Mass Spectrometry. In 10th International Conference on Machine Learning and Applications and Workshops (ICMLA 2011), December 18-21, 2011, Honolulu, Hawaii, 2, 186-188. Piscataway, New Jersey:IEEE.PNNL-SA-79072. doi:10.1109/ICMLA.2011.45

Kurkure U., Y.H. Le, N. Paragios, J.P. Carson, T. Ju, and I. Kakadiaris. 2011. Landmark/Image-based Deformable Registration of Gene Expression Data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), June 20-25, 2011, Colorado Springs, Colorado, 1089-1096. Los Alamitos, California: IEEE Computer Society. PNWD-SA-8905.doi:10.1109/CVPR.2011.5995708

McGrath L.R., K.O. Domico, C.D. Corley, and B.M. Webb-Robertson. 2011. Complex Biological Event Extraction from Full Text using Signatures of Linguistic and Semantic Features. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Proceedings of BioNLP Shared Task 2011Workshop, June 24th, Portland, Oregon, 130-137. Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-79051.

Gregory M.L., L.R. McGrath, E.B. Bell, K.A. O'Hara, and K.O. Domico. 2011. Domain Independent Knowledge Base Population From Structured and Unstructured Data Sources. In Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference, May 18-20, 2011, Palm Beach, Florida, edited by RC Murray and PM McCarthy. Menlo Park, California: AAAI Press. PNWD-SA-9405.

Paulson P.R., T.E. Carroll, C. Sivaraman, P.A. Neorr, S.D. Unwin, and S.S. Hossain. 2011. Simplifying Probability Elicitation and Uncertainty Modeling in Bayesian Networks. In Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), April 16-17, 2011, Cincinnati, OH, edited by S Visa, A Inoue and AL Ralescu, 114-119. Madison, Wisconsin:Omnipress.PNNL-SA-77781.

McDermott J.E., M.N. Costa, S. Stevens, M. Stenzel-Poore, and A.P. Sanfilippo. 2011. DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS. In Biocomputing 2011: Proceedings of the Pacific Symposium on Biocomputing, January 3-7, 2011, Kohala Coast, Hawaii, edited by RB Altman, et al, 314-25. London:World Scientific. PNNL-SA-74078. doi:10.1142/9789814335058_0033

2010

Journal articles

Buchko G.W., G. Niemann, E.S. Baker, M.E. Belov, R.D. Smith, F. Heffron, and J.N. Adkins, et al. 2010. A multi-pronged search for a common structural motif in the secretion signal of Salmonella enterica serovar Typhimurium type III effector proteins. Molecular Biosystems 6, no. 12:2448-2458.PNNL-SA-73691. doi:10.1039/c0mb00097c

Shah A.R., K. Agarwal, E.S. Baker, M. Singhal, A.M. Mayampurath, Y.M. Ibrahim, and L.J. Kangas, et al. 2010. Machine learning based prediction for peptide drift times in ion mobility spectrometry. Bioinformatics 26, no. 13:1601-1607. PNWD-SA-8802. doi:10.1093/bioinformatics/btq245

Conference papers

Joslyn C.A., and E.A. Hogan. 2010. Order Metrics for Semantic Knowledge Systems. In Proceedings of the 5th International Conference on Hybrid Artificial Intelligence Systems, Part II, (HAIS 2010), June 23-25, 2010, San Sebastian, Spain. Lecture Notes in Computer Science, edited by E Corchado, MG Romay and AM Savio, 6077, 399-409. Berlin Heidelberg: Springer. PNWD-SA-8893. doi:10.1007/978-3-642-13803-4_50

Book chapters

Khalil A.F., Y.H. Kaheil, K. Gill, and M. Mckee. 2010. Application of Learning Machines and Combinatorial Algorithms in Water Resources Management and Hydrologic Sciences. In Machine Learning Research Progress. 61-106. Hauppauge, New York: Nova Science.PNNL-SA-58202.

Oehmen C.S., and B.M. Webb-Robertson. 2010. Evaluating the Computational Requirements of using SVM software to train Data-Intensive Problems. In Machine Learning Research Progress, Nova Science Publishers. Hauppauge, New York:Nova Science.PNNL-SA-59493.

2009

Journal articles

Samudrala R., F. Heffron, and J.E. McDermott. 2009. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems. PLoS Pathogens 5, no. 4.PNNL-SA-61526. doi:10.1371/journal.ppat.1000375

Conference papers

Krishnamoorthy N., S. Debray, and A.K. Fligg. 2009. Static Detection of Disassembly Errors. In Proceedings of the 16th Working Conference on Reverse Engineering (WCRE 2009), October 13-16, 2009, Lille, France, 259-268. Los Alamitos, California: IEEE Computer Society.PNNL-SA-67642. doi:10.1109/WCRE.2009.16

Cowell A.J., M.L. Gregory, E.J. Marshall, and L.R. McGrath. 2009. Automated Knowledge Annotation for Dynamic Collaborative Environments. In Proceedings of the 22nd International Florida Artificial Intelligence Research Society (FLAIRS) Conference, edited by HC Lane and HW Guesgen. Menlo Park, California: AAAI Press.PNNL-SA-63853.

Riensche R.M., P.R. Paulson, G.R. Danielson, S.D. Unwin, R.S. Butner, S.M. Miller, and L. Franklin, et al. 2009. Serious Gaming for Predictive Analytics. In AAAI Spring Symposium on Technosocial Predictive Analytics. Menlo Park, California:Association for the Advancement of Artificial Intelligence (AAAI). PNNL-SA-62908.

Webb-Robertson B.M., M.M. Matzke, and C.S. Oehmen. 2009. Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification. In The Seventh International Conference on Machine Learning and Applications, 457-462. Los Alamitos, California: IEEE Computer Society.PNNL-SA-60288.

Book chapters

Webb-Robertson B.M. 2009. Support Vector Machines for Improved Peptide Identification from Tandem Mass Spectrometry Database Search. In Mass Spectrometry of Proteins and peptides: Methods in Molecular Biology Vol 146. New York, New York: Humana Press.PNNL-SA-51841.

2008

Journal articles

Shah A.R., C.S. Oehmen, and B.M. Webb-Robertson. 2008. SVM-Hustle - An iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics 24, no. 6:783-790. PNNL-SA-56589.

Conference papers

Oehmen C.S., and W.R. Cannon. 2008. Bringing high performance computing to the biologist’s workbench: approaches, applications and challenges. In SciDAC 2008: Journal of Physics: Conference Series, 125, 012052. Bristol:IOP Publishing Ltd.PNNL-SA-61076. doi:10.1088/1742-6596/125/1/012052

Xie Y., C.J. Murray, T.P. Hanrahan, and D.R. Geist. 2008. Data Mining on Large Data Set for Predicting Salmon Spawning Habitat. In Proceedings of The 2008 International Conference on Data Mining (DMIN'08), edited by R. Stahlbock, S. F. Crone and S. Lessmann, 1, 233-239. Athens, Nevada :CSREA Press. PNNL-SA-59284.

Miller D.W., S.A. Arndt, D.D. Dudenhoeffer, B.P. Hallbert, B.P. Hallbert, L.J. Bond, and D.E. Holcomb, et al. 2008. Instrumentation and Control and Human Machine Interface Science and Technology Roadmap in Support of Advanced Reactors and Fuel Programs in the U.S. In 2008 International Congress on Advances in Nuclear Power Plants (ICAPP '08), 2, 844-852. La Grange Park, Illinois: American Nuclear Society.PNNL-SA-58812.

Reports

Coleman A.M. 2008. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks Richland, WA: Pacific Northwest National Laboratory.

Book chapters

Taylor R.C., and M. Singhal. 2008. Biological Network Inference and Analysis using SEBINI and CABIN. In Computational Systems Biology. 551-576. New York, New York: Humana Press.PNNL-SA-56049.

2007

Journal articles

Shah A.R., C.S. Oehmen, J.K. Harper, and B.M. Webb-Robertson. 2007. Integrating Subcellular Location for Improving Machine Learning Models of Remote Homology Detection in Eukaryotic Organisms. Computational Biology and Chemistry 31, no. 2:138-142.PNNL-SA-48402. doi:10.1016/j.compbiolchem.2007.02.012

Bishop M., and D. Frincke. 2007. Achieving Learning Objectives through E-Voting Case Studies. IEEE Security & Privacy 5, no. 1:53-56.PNNL-SA-53334. doi:10.1109/MSP.2007.1

Conference papers

Taylor R.C., M. Singhal, D.S. Daly, K.O. Domico, A.M. White, D.L. Auberry, and K.J. Auberry, et al. 2007. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction. In Sixth International Conference on Machine Learning and Applications, (ICMLA 2007), 587-593. Washington DC: IEEE Computer Society.PNNL-SA-55941.doi:10.1109/ICMLA.2007.63

Webb-Robertson B.M., C.S. Oehmen, and W.R. Cannon. 2007. Support Vector Machine Classification of Probability Models and Peptide Features for Improved Peptide Identification from Shotgun Proteomics. In The Sixth International Conference on Machine Learning and Applications (ICMLA ’07), 500-505. Washington DC: IEEE Computer Society.PNNL-SA-58675.doi:10.1109/ICMLA.2007.17

Sanfilippo A.P., A.J. Cowell, S.C. Tratz, A.M. Boek, A.K. Cowell, C. Posse, and L.C. Pouchard. 2007. Content Analysis for Proactive Intelligence: Marshaling Frame Evidence. In Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada, 919-924. Menlo Park, California: AAAI Press.PNNL-SA-54967.

Tratz S.C., and A.P. Sanfilippo. 2007. A High Accuracy Method for Semi-supervised Information Extraction. In Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007), 169-172. East Stroudsburg, Pennsylvania: Association for Computational Linguistics.PNNL-SA-53858.

2006

Conference papers

Ferris K.F., B.M. Webb-Robertson, and D.M. Jones. 2006. Information-Based Development of New Radiation Detectors. In Materials Research Society Symposium Proceedings: Combinatorial Methods and Informatics in Materials Science, edited by MJ Fasolka, et al, 894. Warrendale, Pennsylvania: Materials Research Society. PNNL-SA-47703.

2005

Journal articles

Webb-Robertson B.M., C.S. Oehmen, and M.M. Matzke. 2005. SVM-BALSA: Remote Homology Detection based on Bayesian Sequence Alignment. Computational Biology and Chemistry 29, no. 6:440-3. PNNL-SA-45823.

2004

Conference papers

Whitney P.D., M.R. Weimar, and A.J. Calapristi. 2004. Exploratory Analysis of Transaction Data. In Proceedings of MLMTA'04 - The 2004 International Conference on Machine Learning; Models, Technologies and Applications, Volume 2, 973-976. Bogart, Georgia: CSREA Press.PNNL-SA-40960.

Whitney P.D., M.E. Powers, G. Chin, K.E. Johnson, O.A. Kuchar, and J.M. Sloughter. 2004. A Data Signature Approach for Analyzing, Manipulating and Understanding Collections of Graphical Summaries of Scenarios. In Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04, 2, 1001-1006. Bogart, Georgia: CSREA Press.PNNL-SA-40959.

2003

Conference papers

Savelieva-Trofimova E.A., M. Kanevski, v. timonin, A. Pozdnukhov, C.J. Murray, T.D. Scheibe, and Y. Xie, et al. 2003. Aquifer Hydrogeologic Layer Zonation at the Hanford Site. In Proceedings IAMG 2003, 46+. Kingston, Ont: International Association for Mathematical Geosciences.PNNL-SA-38948.

Solinsky J.C., S.E. Budge, P.D. Majors, and B.B. Rex. 2003. Real-time Image Analysis of Living Cellular-Biology Measurements of Intelligent Chemistry. In Sixth International Conference on Quality Control by Artificial Vision, 5132, 78-90. Bellingham, Washington: SPIE-Int. Society of Optical Engineering. PNNL-SA-38684.

Report

Xie Y., C.J. Murray, G.V. Last, and R.D. Mackley. 2003. Mineralogical and Bulk-Rock Geochemical Signatures of Ringold and Hanford Formation Sediments Richland, WA: Pacific Northwest National Laboratory.

2002

Journal articles

Lubenau J.O., and D.J. Strom. 2002. Safety and security of radiation sources in the aftermath of 11 September 2001. Health Physics 82, no. 2:155-164. PNNL-SA-35874.

Conference papers

Burke J.S., G.R. Danielson, D.A. Schulz, and L.W. Vail. 2002. Parallel computing for automated model calibration. In SCI 2002 : ISAS : the 6th World Multiconference on Systemics, Cybernetics and Informatics : July 14-18, 2002, Orlando, Florida, USA : proceedings, XVIII, 424-429. Orlando, Florida: International Institute of Informatics and Systemics.PNNL-SA-36972.