PNNL

Jenna Pope is a data scientist in the National Security Directorate at Pacific Northwest National Laboratory. Her research focuses on the application of data science and deep learning to chemistry and materials science. Her projects are highly interdisciplinary and involve close collaboration with both experimentalists and modelers/theoreticians. She is a member of the American Chemical Society and serves on review panels for the National Science Foundation. She received a BS in chemistry from the University of West Florida and a PhD in computational chemistry from the University of Georgia. She publishes under the name Jenna A. Bilbrey.

• Chemistry
• Computational Chemistry
• Data Science
• Deep Learning
• Few-shot Learning
• Materials Science
• Machine Learning
• Neural Networks

• PhD in Computational Chemistry, University of Georgia
• BS in Chemistry, University of West Florida

• National Science Foundation reviewer
• American Chemical Society member

• Exceptional Contribution Award, Pacific Northwest National Laboratory, 2021
• Outstanding Performance Award, Pacific Northwest National Laboratory, 2020

2025

• Bilbrey J.A., J.S. Firoz, M. Lee, and S. Choudhury. 2025. "Uncertainty Quantification for Neural Network Potential Foundation Models." npj Computational Materials 11, no. 1:Art. No. 109. PNNL-SA-206884. doi:10.1038/s41524-025-01572-y
• Kaspar T.C., S.M. Akers, H.W. Sprueill, A.H. Ter-Petrosyan, J.A. Bilbrey, D.F. Hopkins, and A.V. Harilal, et al. 2025. "Machine-learning-enabled on-the-fly analysis of RHEED patterns during thin film deposition by molecular beam epitaxy." Journal of Vacuum Science & Technology A: International Journal Devoted to Vacuum, Surfaces, and Films 43, no. 3:Art. No. 032702. PNNL-SA-206700. doi:10.1116/6.0004493

2024

• 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. PNNL-36839. Richland, WA: Pacific Northwest National Laboratory. Unifying Combinatorial and Graphical Methods in Artificial Intelligence
• Bylaska E.J., A.R. Panyala, N.P. Bauman, B. Peng, H. Pathak, D. Mejia-Rodriguez, and N. Govind, et al. 2024. "Electronic structure simulations in the cloud computing environment." The Journal of Chemical Physics 161, no. 15:Art. No. 150902. PNNL-SA-199904. doi:10.1063/5.0226437
• 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
• 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
• Kappagantula K.S., M.Y. Obiri, M. Taufique, L.E. Richards, E. King, S. Howland, and J.A. Bilbrey, et al. 2024. Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework. PNNL-37769. Richland, WA: Pacific Northwest National Laboratory. Materials Characterization, Prediction and Control Project: Summary Report on Data Analytics Framework

2023

• Holden M.J., C.M. Doty, A.H. Ter-Petrosyan, J.A. Bilbrey, S.M. Akers, and S.R. Spurgeon. 2023. Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning. PNNL-35397. Richland, WA: Pacific Northwest National Laboratory. Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning
• 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
• Sprueill H.W., J.A. Bilbrey, Q. Pang, and P.V. Sushko. 2023. "Active Sampling for Neural Network Potentials: Accelerated Simulations of Shear-induced Deformation in Cu-Ni Multilayers." Journal of Chemical Physics 158, no. 11:Art. No. 114103. PNNL-SA-179331. doi:10.1063/5.0133023

2022

• Bilbrey J.A., N. Chen, S. Hu, and P.V. Sushko. 2022. "Graph-Component Approach to Defect Identification in Large Atomistic Simulations." Computational Materials Science 214. PNNL-SA-173243. doi:10.1016/j.commatsci.2022.111700
• Knutson C.R., M.S. Bontha, J.A. Bilbrey, and N. Kumar. 2022. "Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks." Scientific Reports 12. PNNL-SA-166075. doi:10.1038/s41598-022-10418-2

2021

• Alexander F.J., J.A. Ang, J.A. Bilbrey, J. Balewski, T.A. 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

2020

• Bilbrey J.A., C.M. Ortiz Marrero, M. Sassi, A.M. Ritzmann, N.J. Henson, and M. Schram. 2020. "Tracking the chemical evolution of iodine species using recurrent neural networks." ACS Omega 5, no. 9:4588-4594. PNNL-SA-148824. doi:10.1021/acsomega.9b04104
• 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
• Bilbrey J.A., J. Heindel, M. Schram, P. Bandyopadhyay, S.S. Xantheas, and S. Choudhury. 2020. "A Look Inside the Black Box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters." Journal of Chemical Physics 153, no. 2:024302. PNNL-SA-152462. doi:10.1063/5.0009933