PNNL

Dr. Kappagantula is a Senior Scientist and the Team Leader for Functional Materials and Enabling Technologies Team. Her research focuses on developing high-performance materials through the use of nanotechnology, advanced manufacturing processes, atomistic-to-mesoscale modeling, and machine learning/artificial intelligence/data-driven methods. Prior to joining PNNL in 2019, she was an Assistant Professor (tenure-track) of Mechanical Engineering at Ohio University. She also helped manage the Center for Advanced Materials Processing as the Assistant Director.

Dr. Kappagantula manages projects that manufacture metal, polymer, and ceramic composites that are used applications ranging from energy-efficient electric machines to sustainable carbon-negative building materials. She is funded by various DOE Offices, such as the Vehicles Technology Office, the Advanced Materials & Manufacturing Technologies Office, and the Office of Fossil Energy & Carbon Management, as well as by industry partners, to identify materials of interest, develop manufacturing approaches, and transition technologies into commercial use.

• Ph.D., Mechanical Engineering, Texas Tech University
• M.S., Mechanical Engineering, Texas Tech University
• B.S., Mechanical Engineering, Andhra University

• ASM International
  • Member, Inclusion, Diversity, and Equity Awareness (IDEA) committee
  • Member, IMAT Programming Committee
• The Minerals, Metals & Materials Society (TMS)
  • Member, Education Committee
  • Member, Light Metals Committee
  • Member, Nanomaterials Committee

• PNNL Innovation in Research Team Award, Energy Processes and Materials Division, 2023.
• Pathway to Excellence Award, Pacific Northwest National Laboratory, Washington, 2021.
• R&D 100 Award for Shear Assisted Processing and Extrusion (ShAPE^TM), 2020.
• Marilyn E. and Ann D. White Research Award, Department of Mechanical Engineering, Russ College of Engineering and Technology, Ohio University, 2018.
• TTU TRIP Match T. Fuller Scholarship for Fall-2013, Texas Tech University, Texas, 2013.
• NATAS Graduate Student Award, 40th NATAS Conference, Orlando, Florida, 2012.
• Graduate Research Travel Grant, (AIChe Annual Meeting), Texas Tech University, Texas, 2012.
• Graduate Research Travel Grant, (40th NATAS Conference), Texas Tech University, Texas, 2012.
• TTU Harrington Graduate Engineering Scholarship for 2010-2011, Edward E. Whitacre Jr. College of Engineering, Texas Tech University, Texas.
• Department of Mechanical Engineering Scholarship for 2011-2012, Texas Tech University, Texas.
• Outstanding Student Paper Presentation: IndaCon Proceedings 2008, Andhra University, India, 2008.

• U.S. Patent No. 10,369,748, August 6, 2019, "FRICTION STIRRING INTERLOCKING OF DISSIMILAR MATERIALS".
• U.S. Patent No. 11,946,504, April 2, 2024, "Assemblies, Riveted Assemblies, Methods for Affixing Substrates, and Methods for Mixing Materials to Form a Metallurgical Bond (iEdison No. 0685901-22-0263)".
• U.S. Patent No. 11,549,532, January 10, 2023, "Assemblies, Riveted Assemblies, Methods for Affixing Substrates, and Methods for Mixing Materials to Form a Metallurgical Bond (iEdison No. 0685901-22-0263)"

2025

• Koch J.V., W. Choi, E. King, D. Garcia, H. Das, T. Wang, and K.A. Ross, et al. 2025. "Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing." Journal of Intelligent Manufacturing 36, no. 2:1111-1121. PNNL-SA-184214. doi:10.1007/s10845-023-02271-5
• Li L., D. Garcia, T. Wang, J.D. Escobar, M. Pole, K. Nwe, and D.M. Brown, et al. 2025. "Meshfree simulation and prediction of recrystallized grain size in friction stir processed 316L stainless steel." Journal of Materials Processing Technology 337. PNNL-SA-205212. doi:10.1016/j.jmatprotec.2025.118751

2024

• Chowdhury S.A., M. Taufique, J. Wang, M. Masden, M. Wenzlick, R. Devanathan, and A.L. Schemer-Kohrn, et al. 2024. "Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy." Integrating Materials and Manufacturing Innovation 13. PNNL-SA-184695. doi:10.1007/s40192-023-00305-7
• Dagle R.A., J.A. Lopez-Ruiz, N.W. Riedel, K.S. Kappagantula, D. Zhang, and C.A. Fernandez. 2024. Methane Pyrolysis for CO2-free H2 and Carbon Nanomaterials - CRADA 576 (Final Report). PNNL-36217. Richland, WA: Pacific Northwest National Laboratory. Methane Pyrolysis for CO2-free H2 and Carbon Nanomaterials - CRADA 576 (Final Report)
• Frazier W.E., A. Visweswara Sathanur, M. Taufique, R. Devanathan, and K.S. Kappagantula. 2024. "Monte Carlo Simulations of 347H Stainless Steel Aging for the Synthetic Generation of Microstructures Under Creep Conditions." Metallurgical and Materials Transactions A - Physical Metallurgy and Materials Science 55, no. 10:4026-4034. PNNL-SA-177884. doi:10.1007/s11661-024-07477-7
• Frazier W.E., and K.S. Kappagantula. 2024. "A Computational Study of the Effects of Graphene Additions on Electrical Properties of Polycrystalline Copper." Composites Communications 52, no. _:Art no. 102124. PNNL-SA-173715. doi:10.1016/j.coco.2024.102124
• Gwalani B., X. Li, A.K. Nittala, W. Choi, M. Reza E Rabby, J.D. Escobar, and A.J. Bhattacharjee, et al. 2024. "Unprecedented Electrical Performance of Friction-Extruded Copper-Graphene Composites." Materials & Design 237. PNNL-SA-190528. doi:10.1016/j.matdes.2023.112555
• 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." In 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.
• Kappagantula K.S. 2024. Aluminum Ultra-conductors for Energy-Efficient Aerospace Busbar Applications - CRADA 641 (Abstract). PNNL-36406. Richland, WA: Pacific Northwest National Laboratory. Aluminum Ultra-conductors for Energy-Efficient Aerospace Busbar Applications - CRADA 641 (Abstract)
• Kappagantula K.S. 2024. Characterizing Copper-Graphene Composites - CRADA 643 (Abstract). PNNL-36188. Richland, WA: Pacific Northwest National Laboratory. Characterizing Copper-Graphene Composites - CRADA 643 (Abstract)
• Kappagantula K.S. 2024. Microstructural Characterization of Enhanced Conductivity Aluminum Alloys - CRADA 642 (Abstract). PNNL-36189. Richland, WA: Pacific Northwest National Laboratory. Microstructural Characterization of Enhanced Conductivity Aluminum Alloys - CRADA 642 (Abstract)
• Nepal K., C. Ugwumadu, A. Gautam, K.S. Kappagantula, and D. Drabold. 2024. "Electronic Conductivity in Metal-Graphene Composites: The Role of Disordered Carbon Structures, Defects, and Impurities." Journal of Physics: Materials 7, no. 2:Art. No. 025003. PNNL-SA-192067. doi:10.1088/2515-7639/ad261a
• Nepal K., C. Ugwumadu, K. Subedi, K.S. Kappagantula, and D. Drabold. 2024. "Physical origin of enhanced electrical conduction in aluminum-graphene composites." Applied Physics Letters 124, no. 9:Art. No. 091902. PNNL-SA-193743. doi:10.1063/5.0195967
• Nittala A.K., L. Furuta, J.A. Silverstein, A. Poznak, F.F. Kraft, and K.S. Kappagantula. 2024. "Electrical Property Enhancement of Non-Heat-Treatable Wrought Aluminum Alloys Using Graphene Additives." Journal of Alloys and Compounds 1003, no. _:Art. No. 175434. PNNL-SA-194099. doi:10.1016/j.jallcom.2024.175434
• Wang T., Z. Lu, Q. Zhang, A. Saboo, X. Ma, T. Liu, and Z. Mahbooba, et al. 2024. "High-temperature lean Cu alloys with Cr-to-Nb atomic ratio of 2." Materials Today Communications 39, no. _:Art. No. 108884. PNNL-SA-190640. doi:10.1016/j.mtcomm.2024.108884

2023

• Gwalani B., X. Li, A.K. Nittala, W. Choi, M. Reza E Rabby, J.D. Escobar, and A.J. Bhattacharjee, et al. 2023. "Unprecedented Electrical Performance of Friction-Extruded Copper-Graphene Composites." Materials & Design. PNNL-SA-190528. doi.org/10.1016/j.matdes.2023.112555
• Nittala A.K., J. Smith, B. Gwalani, J.A. Silverstein, F.F. Kraft, and K.S. Kappagantula. 2023. "Simultaneously Improved Electrical and Mechanical Performance of Hot-Extruded Bulk Scale Aluminum-Graphene Wires." Materials Science and Engineering B: Advanced Functional Solid-state Materials 293. PNNL-SA-181281. doi:10.1016/j.mseb.2023.116452
• 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
• Subedi K., K. Nepal, C. Ugwumadu, K.S. Kappagantula, and D. Drabold. 2023. "Electronic transport in copper-graphene composites." Applied Physics Letters 122, no. 3:Art. No. 031903. PNNL-SA-180138. doi:10.1063/5.0137086
• Srivastava A., H. Das, D. Ramirez Tamayo, L. Li, M. Pole, B. Gwalani, and A. Soulami, et al. 2023. "Extent of interlocking and metallurgical bonding in friction riveting of aluminum alloy to steel." The International Journal of Advanced Manufacturing Technology 128, no. 7-8:2899–2911. PNNL-SA-180851. doi:10.1007/s00170-023-12111-8
• 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 152^nd 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
• Das H., K.S. Kappagantula, A. Srivastava, P. Upadhyay, J. Fernandez dos Santos, and M. Reza E Rabby. 2023. "Embedded anchoring of multi-material assemblies by friction riveting process." In TMS 2023: Friction Stir Welding and Processing XII Symposium held at the TMS Annual Meeting and Exhibition, March 19-23, 2023. San Diego, CA. Minerals, Metals and Materials Series, edited by Y. Hovanksi, et al, 149–156. Cham:Springer. PNNL-SA-177374. doi:10.1007/978-3-031-22661-8_14
• Brown D.R., C.L. Wight, H.J. Kvinge, and K.S. Kappagantula. 2023. "What Does a Computer Vision Model Trained to Classify Material Microstructure Images Actually Understand?." In 2023 TMS Annual Meeting & Exhibition; AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification. PNNL-SA-182912.

2022

• Gwalani B., M. Pole, K.E. Whalen, S. Li, A. Yu, B.T. O'Callahan, and A.K. Nittala, et al. 2022. "Atomistic Understanding of Extreme Strain Shear Deformation of Copper Graphene Composites." Carbon 198. PNNL-SA-170779. doi:10.1016/j.carbon.2022.07.013
• Li X., T. Wang, X. Ma, N.R. Overman, S.A. Whalen, D.R. Herling, and K.S. Kappagantula. 2022. "Manufacture aluminum alloy tube from powder with a single-step extrusion via ShAPE." Journal of Manufacturing Processes 80. PNNL-SA-169412. doi:10.1016/j.jmapro.2022.05.060
• Subedi K., K.S. Kappagantula, F.F. Kraft, A.K. Nittala, and D. Drabold. 2022. "Electrical conduction processes in aluminum: defects and phonons." Physical Review B 105, no. 10:Art. No. 104114. PNNL-SA-170381. doi:10.1103/PhysRevB.105.104114
• Kappagantula K.S., J. Smith, A.K. Nittala, and F.F. Kraft. 2022. "Macro Copper-Graphene Composites with Enhanced Electrical Conductivity." Journal of Alloys and Compounds 894. PNNL-SA-158464. doi:10.1016/j.jallcom.2021.162477
• Kalsar R., X. Ma, J.T. Darsell, D. Zhang, K.S. Kappagantula, D.R. Herling, and V.V. Joshi. 2022. "Microstructure Evolution, Enhanced Aging Kinetics, and Mechanical Properties of AA7075 Alloy after Friction Extrusion." Materials Science and Engineering A. Structural Materials: Properties, Microstructure and Processing 833. PNNL-SA-166338. doi:10.1016/j.msea.2021.142575
• Wang T., S.A. Whalen, X. Ma, J.A. Silverstein, H. Das, M. Pallaka, and A. Ortiz, et al. 2022. "Friction-based riveting technique for AZ31 magnesium alloy." Journal of Magnesium and Alloys 10, no. 1:110 - 118. PNNL-SA-155089. doi:10.1016/j.jma.2021.06.004
• Frazier W.E., B. Gwalani, J.D. Escobar, J.A. Silverstein, and K.S. Kappagantula. 2022. "A Finite Difference Analysis of the Effect of Graphene Additions on the Electrical Conductivity of Polycrystalline Copper." In Proceedings of the 151st Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, (TMS 2022), February 27- March 3, 2022, Anaheim, CA. Minerals, Metals and Materials Series, 705 - 712. Cham:Springer. PNNL-SA-166346. doi:10.1007/978-3-030-92381-5_67
• Truong L.T., W. Choi, C.L. Wight, E.D. Coda, T.H. Emerson, K.S. Kappagantula, and H.J. Kvinge. 2022. "Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing." In AI for Design and Manufacturing. PNNL-SA-168212.
• 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
• Nittala A.K., F.F. Kraft, A. Poznak, and K.S. Kappagantula. 2022. "Synthesis of Ultra-Conductive Aluminum Alloys with Graphite Nanoparticles by Hot Extrusion Alloying." In Twelfth International Aluminum Extrusion Technology Seminar & Exposition ET' 22 Proceedings. Orlando, Florida:ET Foundation. PNNL-SA-148938.
• 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 6th Workshop on Geometry and Machine Learning. PNNL-SA-172921.
• 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.