Accelerating multiscale materials modeling with machine learning

A cube with balls represents atoms configured on a grid.

Multiscale materials modeling fundamental insight into microscopic mechanisms that determine materials properties in nuclear stockpile applications that leverage radiation harden semiconductors, advanced manufacturing, shock compression, and energetic materials. This LDRD team including three postdoctoral researchers developed a new ML surrogate model for density functional theory using deep neural networks to accurately predict total energies of 100,000 atom systems when trained on only 256 atoms.

When compared with direct numerical simulation of 2048 aluminum atoms, the error provides in electron density of the new surrogate model is under 1%, but computation is three orders of magnitude faster. Promising methodologies such as optimal experimental design techniques and novel Graph Neural Networks were explored in training smaller data sets and will be researched further in the future to continue accelerating first-principal data generation and increase the fidelity and robustness of predictive atomistic materials simulations. An ML model designed for aluminum has already been successfully leveraged in Sandia’s Electronics Parts Program milestone.

CAD model (left) with multiple fasteners (right), rapidly reduced to simulation-ready state using new ML methods.

Sandia researchers linked to work

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Associated Publications

  • Naugle, A., Krofcheck, D., Warrender, C., Lakkaraju, K., Swiler, L., Verzi, S., Emery, B., Murdock, J., Bernard, M., Romero, V., & Romero, V. (2023). What can simulation test beds teach us about social science? Results of the ground truth program. Computational and Mathematical Organization Theory, 29(1), pp. 242-263. https://doi.org/10.1007/s10588-021-09349-6 Publication ID: 80604
  • Naugle, A., Verzi, S., Lakkaraju, K., Swiler, L., Warrender, C., Bernard, M., Romero, V., & Romero, V. (2023). Feedback density and causal complexity of simulation model structure. Journal of Simulation, 17(3), pp. 229-239. https://doi.org/10.1080/17477778.2021.1982653 Publication ID: 75723
  • Fiedler, L., Modine, N., Schmerler, S., Vogel, D., Popoola, G., Thompson, A., Rajamanickam, S., Cangi, A., & Cangi, A. (2022). Predicting the Electronic Structure of Matter on Ultra-Large Scales. https://doi.org/10.2172/1895024 Publication ID: 80390
  • Kononov, A., Lee, C., Pereira dos Santos, T., Robinson, B., Yao, Y., Yao, Y., Andrade, X., Baczewski, A., Constantinescu, E., Correa, A., Kanai, Y., Modine, N., Schleife, A., & Schleife, A. (2022). Electron dynamics in extended systems within real-time time-dependent density-functional theory. MRS communications, 12(6), pp. 1002-1014. https://doi.org/10.1557/s43579-022-00273-7 Publication ID: 80304
  • Kelley, B., Rajamanickam, S., & Rajamanickam, S. (2022). Unified Language Frontend for Physic-Informed AI/ML. https://doi.org/10.2172/1888879 Publication ID: 80261
  • Mariner, P., Debusschere, B., Fukuyama, D., Harvey, J., LaForce, T., Leone, R., Perry, F., Swiler, L., TACONI, A., & TACONI, A. (2022). GDSA Framework Development and Process Model Integration FY2022. https://doi.org/10.2172/1893995 Publication ID: 80378
  • Brooks, D., Swiler, L., Stein, E., Mariner, P., Basurto, E., Portone, T., Eckert, A., Leone, R., & Leone, R. (2022). Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation. Advances in Water Resources, 169. https://doi.org/10.1016/j.advwatres.2022.104310 Publication ID: 80187
  • Modine, N., Stephens, J., Swiler, L., Thompson, A., Vogel, D., Cangi, A., Feilder, L., Rajamanickam, S., & Rajamanickam, S. (2022). Accelerating Multiscale Materials Modeling with Machine Learning. https://doi.org/10.2172/1889336 Publication ID: 80251
  • Swiler, L., Basurto, E., Brooks, D., Eckert, A., Leone, R., Mariner, P., Portone, T., Smith, M., & Smith, M. (2022). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2022). https://doi.org/10.2172/1884909 Publication ID: 80086
  • Naugle, A., Swiler, L., Lakkaraju, K., Verzi, S., Warrender, C., Romero, V., & Romero, V. (2022). Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. https://doi.org/10.2172/1884926 Publication ID: 80095
  • Goff, J., Sievers, C., Wood, M., Thompson, A., & Thompson, A. (2022). Permutation-adapted complete and independent basis for atomic cluster expansion descriptors. https://doi.org/10.2172/1879613 Publication ID: 80036
  • Myers, J., Dunlavy, D., & Dunlavy, D. (2022). A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization. https://doi.org/10.2172/1865529 Publication ID: 80700
  • Naugle, A., Russell, A., Lakkaraju, K., Swiler, L., Verzi, S., Romero, V., & Romero, V. (2022). The Ground Truth Program: Simulations as Test Beds for Social Science Research Methods.. Computational and Mathematical Organization Theory, 29(1), pp. 1-16. https://doi.org/10.1007/s10588-021-09346-9 Publication ID: 80622
  • Trott, C.R., Lebrun-Grandie, D., Arndt, D., Ciesko, J., Dang, V., Ellingwood, N., Gayatri, R., Harvey, E., Hollman, D.S., Ibanez, D., Liber, N., Madsen, J., Miles, J., Poliakoff, D., Powell, A., Rajamanickam, S., Simberg, M., Sunderland, D., Turcksin, B., Wilke, J., & Wilke, J. (2022). Kokkos 3: Programming Model Extensions for the Exascale Era. IEEE Transactions on Parallel and Distributed Systems, 33(4), pp. 805-817. https://doi.org/10.1109/TPDS.2021.3097283 Publication ID: 79057
  • Moon, G.E., Kwon, H., Jeong, G., Chatarasi, P., Rajamanickam, S., Krishna, T., & Krishna, T. (2022). Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication. IEEE Transactions on Parallel and Distributed Systems, 33(4), pp. 1002-1014. https://doi.org/10.1109/TPDS.2021.3104240 Publication ID: 79857
  • Fiedler, L., Hoffmann, N., Mohammed, P., Popoola, G., Yovell, T., Oles, V., Ellis, A., Rajamanickam, S., Cangi, A., & Cangi, A. (2022). Finding Electronic Structure Machine Learning Surrogates without Training. https://doi.org/10.2172/1891948 Publication ID: 80361
  • Thompson, A., Aktulga, H.M., Berger, R., Bolintineanu, D., Brown, W.M., Crozier, P.S., in ‘t Veld, P.J., Kohlmeyer, A., Moore, S., Nguyen, T.D., Shan, R., Stevens, M., Tranchida, J., Trott, C.R., Plimpton, S.J., & Plimpton, S.J. (2022). LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications, 271. https://doi.org/10.1016/j.cpc.2021.108171 Publication ID: 80783
  • Yasar, A., Rajamanickam, S., Berry, J.W., Catalyurek, U.V., & Catalyurek, U.V. (2022). A Block-Based Triangle Counting Algorithm on Heterogeneous Environments. IEEE Transactions on Parallel and Distributed Systems, 33(2), pp. 444-458. https://doi.org/10.1109/tpds.2021.3093240 Publication ID: 79132
  • Lopez, O., Lehoucq, R., Dunlavy, D., & Dunlavy, D. (2022). Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data. https://doi.org/10.2172/1841834 Publication ID: 79989
  • Heinlein, A., Perego, M., Rajamanickam, S., & Rajamanickam, S. (2022). FROSch PRECONDITIONERS FOR LAND ICE SIMULATIONS OF GREENLAND AND ANTARCTICA. SIAM Journal on Scientific Computing, 44(2), pp. B339-B367. https://doi.org/10.1137/21m1395260 Publication ID: 79975
  • Swiler, L. (2021). Uncertainty Quantification (UQ) and Sensitivity Analysis (SA) in GDSA [Presentation]. https://www.osti.gov/biblio/1901835 Publication ID: 77036
  • Lysogorskiy, Y., Oord, C., Bochkarev, A., Menon, S., Rinaldi, M., Hammerschmidt, T., Mrovec, M., Thompson, A., Csányi, G., Ortner, C., Drautz, R., & Drautz, R. (2021). Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon. npj Computational Materials, 7(1). https://doi.org/10.1038/s41524-021-00559-9 Publication ID: 76410
  • Nikolov, S., Wood, M., Cangi, A., Maillet, J.-B., Marinica, M.-C., Thompson, A., Desjarlais, M., Tranchida, J., & Tranchida, J. (2021). Data-driven magneto-elastic predictions with scalable classical spin-lattice dynamics [Presentation]. npj Computational Materials. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115853250&origin=inward Publication ID: 78587
  • Dunlavy, D., Chew, P., & Chew, P. (2021). Document Retrieval and Ranking using Similarity Graph Mean Hitting Times. https://doi.org/10.2172/1835671 Publication ID: 77129
  • Rajamanickam, S., Berger-Vergiat, L., Boman, E., Yamazaki, I., & Yamazaki, I. (2021). Sake December 2021 ECP ST Project Review [Presentation]. https://www.osti.gov/biblio/1902027 Publication ID: 77055
  • Rajamanickam, S. (2021). Can Scientific Software Development Use the Outsourcing Model Successfully? [Conference Presenation]. https://doi.org/10.2172/1908776 Publication ID: 77136
  • Swiler, L. (2021). Verification and Validation for Cyber Emulation [Conference Presenation]. https://doi.org/10.2172/1897016 Publication ID: 76651
  • Adams, B., Bohnhoff, W., Dalbey, K., Ebeida, M., Eddy, J., Eldred, M., Hooper, R., Hough, P., Hu, K., Jakeman, J., Khalil, M., Maupin, K., Monschke, J., Ridgway, E., Rushdi, A., Seidl, D., Stephens, J., Swiler, L., Tran, A., Winokur, J., & Winokur, J. (2021). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User’s Manual). https://doi.org/10.2172/1868142 Publication ID: 80729
  • Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2021). Development of SNASP Machine Learned Interatomic Potentials for Materials in Extreme Environments [Conference Presenation]. https://doi.org/10.2172/1899237 Publication ID: 76870
  • Rajamanickam, S. (2021). Portability in Trilinos [Presentation]. https://www.osti.gov/biblio/1895552 Publication ID: 76522
  • Hughes, C., Ashraf, R., Gioiosa, R., Phillips, C., Berry, J.W., Hart, W., Laird, C., Rajamanickam, S., & Rajamanickam, S. (2021). ARIAA Update — SST [Presentation]. https://www.osti.gov/biblio/1897599 Publication ID: 76739
  • Rajamanickam, S., Rajamanickam, S., & Rajamanickam, S. (2021). Can Scientific Software Development Use the Outsourcing Model Successfully [Conference Paper]. https://www.osti.gov/biblio/1899526 Publication ID: 76879
  • Loe, J.A., Rajamanickam, S., & Rajamanickam, S. (2021). Mixed Precision in Trilinos [Conference Presenation]. https://doi.org/10.2172/1900352 Publication ID: 76971
  • Rajamanickam, S., Heinlein, A., Thornquist, H., Yamazaki, I., & Yamazaki, I. (2021). Trilinos User Group MeetingSolvers Update [Conference Presenation]. https://doi.org/10.2172/1900353 Publication ID: 76972
  • Heinlein, A., Perego, M., Rajamanickam, S., Yamazaki, I., & Yamazaki, I. (2021). FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica [Conference Presenation]. https://doi.org/10.2172/1900354 Publication ID: 76982
  • Thompson, A. (2021). FusMatML End-of-Year Review Summary [Presentation]. https://www.osti.gov/biblio/1897015 Publication ID: 76411
  • Kelley, B., Rajamanickam, S., & Rajamanickam, S. (2021). Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening [Conference Paper]. https://doi.org/10.1109/IPDPS53621.2022.00035 Publication ID: 76347
  • Rajamanickam, S. (2021). Enabling Science Simulations with Scalable Computational Frameworks for Scientific Computing [Presentation]. https://www.osti.gov/biblio/1894020 Publication ID: 76394
  • Littlewood, D., Wood, M., Montes de Oca Zapiain, D., Rajamanickam, S., Trask, N., & Trask, N. (2021). Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]. https://doi.org/10.2172/1828106 Publication ID: 76348
  • Moore, S., Thompson, A., & Thompson, A. (2021). Large-Scale Atomistic Simulations [Slides]. https://doi.org/10.2172/1820306 Publication ID: 75672
  • Mariner, P., Berg, T., Debusschere, B., Eckert, A., Harvey, J., LaForce, T., Leone, R., Mills, M., Nole, M., Park, H., Perry, &., Seidl, D., Swiler, L., Chang, K.W., & Chang, K.W. (2021). GDSA Framework Development and Process Model Integration FY2021. https://doi.org/10.2172/1825056 Publication ID: 76168
  • Swiler, L., Becker, D., Brooks, D., Govaerts, J., Koskinen, L., Plischke, E., Röhlig, K., Saveleva, E., Spiessl, S., Stein, E., Svitelman, V., & Svitelman, V. (2021). Sensitivity Analysis Comparisons on Geologic Case Studies: An International Collaboration. https://doi.org/10.2172/1822591 Publication ID: 75545
  • Pinar, A., Tarman, T., Swiler, L., Gearhart, J., Hart, D., Vugrin, E., Cruz, G., Arguello, B., Geraci, G., Debusschere, B., Hanson, S., Outkin, A., Thorpe, J., Hart, W., Sahakian, M., Gabert, K., Glatter, C., Johnson, E., Punla-Green, S., & Punla-Green, S. (2021). Science & Engineering of Cyber Security by Uncertainty Quantification and Rigorous Experimentation (SECURE) HANDBOOK. https://doi.org/10.2172/1820527 Publication ID: 75697
  • Swiler, L., Brooks, D., & Brooks, D. (2021). Joint Sensitivity Analysis (JOSA) Exercise Meeting Sept. 21, 2021 [Presentation]. https://www.osti.gov/biblio/1888135 Publication ID: 75754
  • Swiler, L. (2021). White paper on Verification and Validation for Cyber Emulation Models. https://doi.org/10.2172/1854720 Publication ID: 75981
  • Wagman, B., Swiler, L., Chowdhary, K., Hillman, B., & Hillman, B. (2021). The Fingerprints of Stratospheric Aerosol Injection in E3SM. https://doi.org/10.2172/1821542 Publication ID: 75727
  • Pinar, A., Tarman, T., Swiler, L., Gearhart, J., Hart, D., Vugrin, E., Cruz, G., Arguello, B., Geraci, G., Debusschere, B., Hanson, S., Outkin, A., Thorpe, J., Hart, W., Sahakian, M., Gabert, K., Glatter, C., Johnson, E., Punla-Green, S., & Punla-Green, S. (2021). Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) (Final Report). https://doi.org/10.2172/1821322 Publication ID: 75817
  • Stickland, M., Li, J., Swiler, L., Tarman, T., & Tarman, T. (2021). Foundations of Rigorous Cyber Experimentation. https://doi.org/10.2172/1854751 Publication ID: 76007
  • Cusentino, M., Bobbitt, N.S., Wood, M., Thompson, A., & Thompson, A. (2021). Development of SNAP Interatomic Potentials for Studying Mixed Materials Effects at the Tungsten Divertor [Conference Presenation]. https://doi.org/10.2172/1890848 Publication ID: 75965
  • Olivier, S.L., Ellingwood, N., Berry, J.W., Dunlavy, D., & Dunlavy, D. (2021). Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications [Conference Presenation]. https://doi.org/10.2172/1887725 Publication ID: 75703
  • Geronimo Anderson, S., Teranishi, K., Dunlavy, D., Choi, J., & Choi, J. (2021). Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures [Conference Presenation]. https://doi.org/10.2172/1888390 Publication ID: 75757
  • Myers, J., Dunlavy, D., & Dunlavy, D. (2021). Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods Beyond Computational Efficiency [Conference Presenation]. https://doi.org/10.2172/1888651 Publication ID: 75760
  • Jeong, G., Kestor, G., Chatarsi, P., Parashar, A., Tsai, P., Rajamanickam, S., Gioiosa, R., Krishna, T., & Krishna, T. (2021). Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators [Conference Presenation]. https://doi.org/10.2172/1890906 Publication ID: 75669
  • Garg, R., Qin, E., Martinez, F., Guirado, R., Jain, A., Abadal, S., Abellan, J., Acacio, M., Alarcon, E., Rajamanickam, S., Krishna, T., & Krishna, T. (2021). Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators. https://doi.org/10.2172/1821960 Publication ID: 75867
  • Acer, S., Boman, E., Glusa, C., Rajamanickam, S., & Rajamanickam, S. (2021). Sphynx: A parallel multi-GPU graph partitioner for distributed-memory systems [Conference Presenation]. Parallel Computing. https://doi.org/10.2172/1853867 Publication ID: 77409
  • Abdelfattah, A., Anzt, H., Ayala, A., Boman, E., Carson, E., Cayrols, S., Cojean, T., Dongarra, J., Falgout, R., Gates, M., Gr\”{u}tzmacher, T., Higham, N., Kruger, S., Li, S., Lindquist, N., Liu, Y., Loe, J.A., Nayak, P., Osei-Kuffuor, D., … Yang, U. (2021). Advances in Mixed Precision Algorithms: 2021 Edition. https://doi.org/10.2172/1814447 Publication ID: 75285
  • Swiler, L., Basurto, E., Brooks, D., Eckert, A., Leone, R., Mariner, P., Portone, T., Smith, M., Stein, E., & Stein, E. (2021). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021). https://doi.org/10.2172/1855018 Publication ID: 79851
  • Tarman, T., Rollins, T., Swiler, L., Cruz, G., Vugrin, E., Huang, H., Sahu, A., Wlazlo, P., Goulart, A., Davis, K., & Davis, K. (2021). Comparing reproduced cyber experimentation studies across different emulation testbeds [Conference Paper]. ACM International Conference Proceeding Series. https://doi.org/10.1145/3474718.3474725 Publication ID: 78420
  • Swiler, L., Brooks, D., Stein, E., Röhlig, K., Plischke, E., Becker, D., Spiessl, S., Koskinen, L., Govaerts, J., Svitelman, V., Saveleva., E., & Saveleva., E. (2021). Metamodelling sensitivity approaches versus regression and graphical methods on the basis of Geologic Cases: An International Collaboration [Conference Presenation]. https://doi.org/10.2172/1884666 Publication ID: 75431
  • Tarman, T., Swiler, L., Vugrin, E., Rollins, T., Cruz, G., Huang, H., Sahu, A., Wlazlo, P., Goulart, A., Davis, K., & Davis, K. (2021). Comparing reproduced cyber experimentation studies across different emulation testbeds [Conference Presenation]. https://doi.org/10.2172/1881645 Publication ID: 79697
  • Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2021). Development of SNAP Potentials for Fusion Reactor Materials [Conference Presenation]. https://doi.org/10.2172/1882069 Publication ID: 79641
  • Wood, M., Thompson, A., Cusentino, M., Montes de Oca Zapiain, D., Oleynik, I., & Oleynik, I. (2021). Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials [Conference Presenation]. https://doi.org/10.2172/1883516 Publication ID: 79748
  • Rice, A., Crawford, M., Armstrong, A., Allerman, A., Modine, N., & Modine, N. (2021). Defect Spectroscopy and Reduced Compensation of UV Illuminated MOCVD n-type GaN [Conference Presenation]. https://doi.org/10.2172/1888973 Publication ID: 79658
  • Geronimo Anderson, S., Teranishi, K., Dunlavy, D., Choi, J., & Choi, J. (2021). Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures [Conference Paper]. https://www.osti.gov/biblio/1884665 Publication ID: 75426
  • Acer, S., Boman, E., Glusa, C., Rajamanickam, S., & Rajamanickam, S. (2021). Sphynx: a parallel multi-GPU graph partitioner [Conference Presenation]. https://doi.org/10.2172/1882077 Publication ID: 79650
  • Ellis, J.A., Fiedler, L., Popoola, G.A., Modine, N., Stephens, J., Thompson, A., Cangi, A., Rajamanickam, S., & Rajamanickam, S. (2021). Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks. Physical Review B. https://doi.org/10.2172/1817970 Publication ID: 79009
  • Moon, G., Kwon, H., Jeong, G., Chatarsi, P., Rajamanickam, S., Krishna, T., & Krishna, T. (2021). Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication. https://doi.org/10.2172/1808019 Publication ID: 78992
  • Tarman, T., Swiler, L., Cruz, G., Vugrin, E., Rollins, T., Huang, H., Sahu, A., Wlazlo, P., Goulart, A., Davis, K., & Davis, K. (2021). Comparing reproduced cyber experimentation studies across different emulation testbeds [Conference Proceeding]. https://doi.org/10.1145/3474718.3474725 Publication ID: 79240
  • Geraci, G., Swiler, L., Debusschere, B., & Debusschere, B. (2021). Multifidelity UQ sampling for Stochastic Simulations [Conference Presenation]. https://doi.org/10.2172/1889573 Publication ID: 79490
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2021). Properties of GMRES with Iterative Refinement on GPUs [Conference Presenation]. https://doi.org/10.2172/1884157 Publication ID: 79139
  • Rajamanickam, S., Berger-Vergiat, L., Dang, V., Ellingwood, N., Harvey, E., Kelley, B., Trott, C.R., & Trott, C.R. (2021). Kokkos Kernels 3.4 [Presentation]. https://www.osti.gov/biblio/1889057 Publication ID: 79353
  • Lennon, K., Rajamanickam, S., & Rajamanickam, S. (2021). Learning Transferable DFT Neural Network Surrogates [Presentation]. https://www.osti.gov/biblio/1884431 Publication ID: 79432
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2021). Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs [Conference Paper]. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 – In conjunction with IEEE IPDPS 2021. https://doi.org/10.1109/IPDPSW52791.2021.00078 Publication ID: 77887
  • Kramer, S., Bolintineanu, D., Long, K., Hamel, C., Frankel, A., Jones, R.E., Swiler, L., Johnson, K., & Johnson, K. (2021). Mechanics of Materials Utilizing Machine Learning: Examples at Sandia National Laboratories [Conference Presenation]. https://doi.org/10.2172/1867564 Publication ID: 78396
  • Stickland, M., Li, J., Tarman, T., Swiler, L., & Swiler, L. (2021). Uncertainty quantification in cyber experimentation [Conference Paper]. https://www.osti.gov/biblio/1867999 Publication ID: 78424
  • Swiler, L. (2021). Sensitivity Analysis for the Latest Crystalline Reference Case [Presentation]. https://www.osti.gov/biblio/1868007 Publication ID: 78432
  • Swiler, L., Brooks, D., & Brooks, D. (2021). GP and PCE surrogate models for estimation of sensitivity indices [Presentation]. https://www.osti.gov/biblio/1868008 Publication ID: 78433
  • Tranchida, J., Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2021). First-principles and classical computational study of W/ZrC interfaces [Conference Poster]. https://doi.org/10.2172/1866898 Publication ID: 78336
  • Qin, E., Jeong, G., Won, W., Kao, S.-C., Kwon, H., Srinivasan, S., Das, D., Moon, G.E., Rajamanickam, S., Krishna, T., & Krishna, T. (2021). Extending sparse tensor accelerators to support multiple compression formats [Conference Paper]. Proceedings – 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021. https://doi.org/10.1109/IPDPS49936.2021.00110 Publication ID: 75805
  • Fiedler, L., Ellis, A., Rajamanickam, S., Cangi, A., & Cangi, A. (2021). An Introduction to the Materials Learning Algorithms Package (MALA) [Conference Poster]. https://doi.org/10.2172/1867139 Publication ID: 78371
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2021). Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs [Conference Presenation]. https://doi.org/10.2172/1869548 Publication ID: 78515
  • Halappanavar, M., Acer, S., Boman, E., Buluc, A., Ekanayate, S., Feerdous, S., Gawande, N., Ghosh, S., Khan, A., Minotoli, M., pothen, A., Rajamanickam, S., Selvitopi, O., Tallent, N., Tumeo, A., & Tumeo, A. (2021). Exagraph: Combinatorial Methods for Enabling Exascale Science [Presentation]. https://www.osti.gov/biblio/1870360 Publication ID: 78618
  • Swiler, L. (2021). Statistical Methods used in the Born Qualified LDRD Project [Presentation]. https://www.osti.gov/biblio/1863692 Publication ID: 78088
  • Cusentino, M., Wood, M.A., Thompson, A., & Thompson, A. (2021). Beryllium-driven structural evolution at the divertor surface. Nuclear Fusion, 61(4). https://doi.org/10.1088/1741-4326/abe7bd Publication ID: 77317
  • Cusentino, M., Wood, M., Wong, C., Kolasinski, R., Wirth, B., Thompson, A., & Thompson, A. (2021). Molecular Dynamics Simulations of Hydrogen and Nitrogen on Tungsten Surfaces [Conference Poster]. https://doi.org/10.2172/1866199 Publication ID: 78291
  • Wong, C., Kolasinski, R., Whaley, J., Cusentino, M., Wood, M., Wirth, B., Thompson, A., & Thompson, A. (2021). Nitrogen effects on hydrogen adsorption at tungsten surfaces [Conference Poster]. https://doi.org/10.2172/1866560 Publication ID: 78328
  • Poliakoff, D., Powell, A., Madsen, C., Ridgway, E., LeBrun-Grandie, D., Rajamanickam, S., Trott, C., & Trott, C. (2021). Kokkos Tools and Performance Tracking [Conference Poster]. https://doi.org/10.2172/1862779 Publication ID: 77974
  • Sprague, M., Ananthan, S., Binyahib, R., Brazell, M., de Frahan, M., King, R., Mullowney, P., Rood, J., Sharma, A., Thomas, S., Vijayakumar, G., Crozier, P.S., Berger-Vergiat, L., Cheung, L.C., Dement, D., Develder, N., Glaze, D., Hu, J., Knaus, R., … Sitaraman, &. (2021). ExaWind: Exascale Predictive Wind Plant Flow Physics Modeling [Conference Poster]. https://doi.org/10.2172/1863503 Publication ID: 78075
  • Rajamanickam, S., Berger-Vergiat, L., Boman, E., Yamazaki, I., & Yamazaki, I. (2021). Sake: Solvers and Kernels for Exascale [Conference Presenation]. https://doi.org/10.2172/1863702 Publication ID: 78099
  • Berger-Vergiat, L., Rajamanickam, S., Dang, V., Ellingwood, N., Kelley, B., Harvey, E., Wilke, J., Acer, S., & Acer, S. (2021). Kokkos Kernels: FY20 update [Conference Presenation]. https://doi.org/10.2172/1863703 Publication ID: 78100
  • Boman, E., Devine, K., Rajamanickam, S., Acer, S., Bogle, I., Slota, G., Madduri, K., Gilbert, M., & Gilbert, M. (2021). ExaGraph: Partitioning and Coloring [Presentation]. https://www.osti.gov/biblio/1882034 Publication ID: 78195
  • Rajamanickam, S., Berger-Vergiat, L., Yamazaki, I., Boman, E., & Boman, E. (2021). Sake: Solvers and Kernels for Exascale [Conference Poster]. https://doi.org/10.2172/1877842 Publication ID: 78203
  • Rajamanickam, S., Berger-Vergiat, L., Acer, S., Dang, V., Ellingwood, N., Harvey, E., Kelley, B., Wilke, J., & Wilke, J. (2021). Kokkos Kernels: FY20 update [Conference Presenation]. https://doi.org/10.2172/1884233 Publication ID: 78204
  • Hammond, S.D., Curry, M., Davis, K., Dang, V., Guba, O., Hoekstra, R., Laros, J.H., Pedretti, K., Poliakoff, D., Rajamanickam, S., Trott, C.R., Younge, A.J., & Younge, A.J. (2021). Fugaku and A64FX Update – April 2021 [Presentation]. https://www.osti.gov/biblio/1882368 Publication ID: 78205
  • Moore, S., Thompson, A., & Thompson, A. (2021). Large-Scale Atomistic Simulations [Slides]. https://doi.org/10.2172/1773391 Publication ID: 77802
  • Porter, N., Maupin, K., Swiler, L., Mousseau, V., & Mousseau, V. (2021). Validation Metrics for Fixed Effects and Mixed-Effects Calibration. Journal of Verification, Validation and Uncertainty Quantification, 6(1). https://doi.org/10.1115/1.4049534 Publication ID: 74591
  • Swiler, L. (2021). Epistemic Uncertainty: Computation and Usage [Conference Presenation]. https://doi.org/10.2172/1855744 Publication ID: 77617
  • Tran, A., Tranchida, J., Wildey, T., Thompson, A., & Thompson, A. (2021). Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys [Conference Poster]. https://doi.org/10.2172/1853874 Publication ID: 77392
  • Plimpton, S.J., Thompson, A., Wood, M., & Wood, M. (2021). LAMMPS as a tool in materials modeling workflows [Conference Presenation]. https://doi.org/10.2172/1853877 Publication ID: 77396
  • Wood, M., Sievers, C., Thompson, A., Lubbers, N., Danny, P., & Danny, P. (2021). Building a Better Database to Learn From; Application to Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/1853859 Publication ID: 77408
  • Ellis, J., Rajamanickam, S., Modine, N., Thompson, A., Stephens, J., Cangi, A., & Cangi, A. (2021). Accelerating Multiscale Materials Modeling with Machine Learning [Conference Presenation]. https://doi.org/10.2172/1853873 Publication ID: 77423
  • Thompson, A. (2021). Generalization of SNAP to Arbitrary Machine-Learning Interatomic Potentials in LAMMPS [Conference Presenation]. https://doi.org/10.2172/1856085 Publication ID: 77680
  • Lysogorskiy, Y., Rinaldi, M., Menon, S., van der Oord, C., Hammerschmidt, T., Mrovec, M., Thompson, A., Csanyi, G., Ortner, C., Drautz, R., & Drautz, R. (2021). Performant implementation of the atomic cluster expansion. https://doi.org/10.2172/1772296 Publication ID: 77681
  • Dunlavy, D., Dunlavy, D., & Dunlavy, D. (2021). Questa High Performance Data Analytics [Presentation]. https://www.osti.gov/biblio/1855745 Publication ID: 77618
  • Fox, J., Gonzalez, B., Ramprasad, R., Rajamanickam, S., Song, L., & Song, L. (2021). Concentric Spherical GNN for 3D Representation Learning [Conference Presenation]. https://doi.org/10.2172/1854067 Publication ID: 77431
  • Kelley, B., Rajamanickam, S., & Rajamanickam, S. (2021). Graph Coarsening Techniques for GPUs and Manycore CPUs [Conference Presenation]. https://doi.org/10.2172/1854071 Publication ID: 77434
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2021). Multiprecision Krylov Solvers in Kokkos and Belos [Conference Presenation]. https://doi.org/10.2172/1854310 Publication ID: 77452
  • Moon, G., Rajamanickam, S., & Rajamanickam, S. (2021). Mixed-Precision Schemes for Linear Algebra Kernels on GPUs [Conference Presenation]. https://doi.org/10.2172/1854428 Publication ID: 77471
  • Anzt, H., Loe, J.A., Rajamanickam, S., & Rajamanickam, S. (2021). xSDK Focus Effort Developing Multiprecision Numerics [Conference Poster]. https://doi.org/10.2172/1856293 Publication ID: 77691
  • Garg, R., Qin, E., Martinez, F., Guirado, R., Jain, A., Abadal, S., Abellan, J., Acacio, M., Alarcon, E., Rajamanickam, S., Krishna, T., & Krishna, T. (2021). A Taxonomy for Classification and Comparison of Dataflows for GNN Accelerators. https://doi.org/10.2172/1817326 Publication ID: 77576
  • Fox, J., Zhao, B., Rajamanickam, S., Ramprasad, R., Song, L., & Song, L. (2021). Concentric Spherical GNN for 3D Representation Learning. https://doi.org/10.2172/1772205 Publication ID: 77664
  • Lewis, C., Hughes, C., Hammond, S.D., Rajamanickam, S., & Rajamanickam, S. (2021). Using MLIR Framework for Codesign of ML Architectures Algorithms and Simulation Tools. https://doi.org/10.2172/1764336 Publication ID: 75211
  • Swiler, L. (2021). Epistemic Uncertainty: Computation and Usage [Conference Presenation]. https://doi.org/10.2172/1845192 Publication ID: 76636
  • Swiler, L., Gulian, M., Frankel, A., Safta, C., Jakeman, J., & Jakeman, J. (2021). Constrained Gaussian Processes: A Survey [Conference Presenation]. https://doi.org/10.2172/1847480 Publication ID: 77280
  • Portone, T., Swiler, L., Geraci, G., Eldred, M., & Eldred, M. (2021). Application of Multifidelity Uncertainty Quantification Methods to a Subsurface Transport Model [Conference Presenation]. https://doi.org/10.2172/1847219 Publication ID: 77339
  • Debusschere, B., Geraci, G., Jakeman, J., Safta, C., Swiler, L., & Swiler, L. (2021). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments [Conference Presenation]. https://doi.org/10.2172/1847628 Publication ID: 77383
  • Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2021). Development of Machine Learned SNAP Potentials for Studying Radiation Damage in Materials [Conference Presenation]. https://doi.org/10.2172/1847209 Publication ID: 77316
  • Gioiosa, R., Rajamanickam, S., Krishna, T., & Krishna, T. (2021). ARIAA: Center for co-design of ARtificial Intelligence focused Architectures and Algorithms [Conference Paper]. https://www.osti.gov/biblio/1847620 Publication ID: 77225
  • Rajamanickam, S., Krishna, T., Hammond, S.D., & Hammond, S.D. (2021). Vision for Co-designing a Unified-Memory Centric Heterogeneous Node Architecture [Conference Paper]. https://www.osti.gov/biblio/1847621 Publication ID: 77226
  • Zinser, B.F., Blake, S., Pfeiffer, R.A., Huang, A., Himbele, J., Freno, B., Dang, V., Kotulski, J., Rajamanickam, S., Johnson, W., Campione, S., Langston, W., & Langston, W. (2021). Gemma: An Electromagnetic Code for Heterogeneous Computer Architectures [Presentation]. https://www.osti.gov/biblio/1847565 Publication ID: 77268
  • Swiler, L. (2021). Uncertainty and Sensitivity Analysis Overview [Presentation]. https://www.osti.gov/biblio/1840824 Publication ID: 75026
  • Swiler, L., Newman, S., Staid, A., Barrett, E., & Barrett, E. (2021). Dakota-NAERM Integration. https://doi.org/10.2172/1762833 Publication ID: 75059
  • Swiler, L. (2021). Uncertainty Analysis of a Medical Resource Demand Model [Presentation]. https://www.osti.gov/biblio/1841815 Publication ID: 75093
  • Geraci, G., Crussell, J., Swiler, L., Debusschere, B., & Debusschere, B. (2021). Exploration of multifidelity UQ sampling strategies for computer network applications. International Journal for Uncertainty Quantification, 11(1), pp. 55-91. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2021033774 Publication ID: 76015
  • Laity, G., Robinson, A., Cuneo, M., Alam, M., Beckwith, K., Bennett, N., Bettencourt, M.T., Bond, S., Cochrane, K., Criscenti, L.J., Cyr, E., De Zetter, K., Drake, R., Evstatiev, E., Fierro, A., Gardiner, T., Glines, F., Goeke, R., Hamlin, N., … McBride, R. (2021). Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report). https://doi.org/10.2172/1813907 Publication ID: 75144
  • Olivier, S.L., Ellingwood, N., Berry, J.W., Dunlavy, D., & Dunlavy, D. (2021). Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications [Conference Paper]. 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021. https://doi.org/10.1109/HPEC49654.2021.9622869 Publication ID: 75415
  • Myers, J., Dunlavy, D., & Dunlavy, D. (2021). Using Computation Effectively for Scalable Poisson Tensor Factorization: Comparing Methods beyond Computational Efficiency [Conference Paper]. 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021. https://doi.org/10.1109/HPEC49654.2021.9622795 Publication ID: 75420
  • Dang, V., Kotulski, J., Rajamanickam, S., & Rajamanickam, S. (2021). ADELUS: A Performance-Portable Dense LU Solver for Distributed-Memory Hardware-Accelerated Systems [Conference Paper]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-030-74224-9_5 Publication ID: 71441
  • Dang, V., Kotulski, J., Rajamanickam, S., & Rajamanickam, S. (2021). ADELUS: A Performance-Portable Dense LU Solver for Distributed-Memory Hardware-Accelerated Systems [Conference Proceeding]. https://www.osti.gov/biblio/1841514 Publication ID: 75074
  • Heinlein, A., Perego, M., Rajamanickam, S., & Rajamanickam, S. (2021). FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica. https://doi.org/10.2172/1763446 Publication ID: 75145
  • Jeong, G., Kestor, G., Chatarasi, P., Parashar, A., Tsai, P.-A., Rajamanickam, S., Gioiosa, R., Krishna, T., & Krishna, T. (2021). Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators [Conference Paper]. Parallel Architectures and Compilation Techniques – Conference Proceedings, PACT. https://doi.org/10.1109/PACT52795.2021.00010 Publication ID: 75221
  • Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2020). Machine Learned SNAP Potentials for Materials Modeling [Presentation]. https://www.osti.gov/biblio/1837138 Publication ID: 72253
  • Cusentino, M., Wood, M.A., Thompson, A., & Thompson, A. (2020). Suppression of helium bubble nucleation in beryllium exposed tungsten surfaces. Nuclear Fusion, 60(12). https://doi.org/10.1088/1741-4326/abb148 Publication ID: 74702
  • Yu, W., Elias, J.A., Chen, K.-W., Baumbach, R., Nenoff, T.M., Modine, N., Pan, W., Henriksen, E.A., & Henriksen, E.A. (2020). Electronic transport properties of a lithium-decorated ZrTe5 thin film. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-60545-x Publication ID: 70603
  • Rajamanickam, S. (2020). Towards simulations on the Exascale hardware and beyond [Presentation]. https://www.osti.gov/biblio/1835223 Publication ID: 72128
  • Loe, J.A., Rajamanickam, S., Boman, E., Anzt, H., & Anzt, H. (2020). ECP Multiprecision Project Review Slides [Presentation]. https://www.osti.gov/biblio/1835661 Publication ID: 72171
  • Rajamanickam, S. (2020). ECP Review : CLOVER Kokkos Kernels [Presentation]. https://www.osti.gov/biblio/1835974 Publication ID: 72208
  • Heinlein, A., Perego, M., Rajamanickam, S., & Rajamanickam, S. (2020). FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica [Conference Presenation]. https://doi.org/10.2172/1835979 Publication ID: 72213
  • Glusa, C., Boman, E., Chow, E., Rajamanickam, S., Szyld, D.B., & Szyld, D.B. (2020). Scalable asynchronous domain decomposition solvers. SIAM Journal on Scientific Computing, 42(6), pp. C384-C409. https://doi.org/10.1137/19m1291303 Publication ID: 74483
  • Lane, J.M.D., Thompson, A., Srivastava, I., Grest, G., Ao, T., Stoltzfus, B.S., Austin, K.N., Fan, H., Morgan, D., Knudson, M., & Knudson, M. (2020). Scale and rate in CdS pressure-induced phase transition. AIP Conference Proceedings, 2272. https://doi.org/10.1063/12.0001041 Publication ID: 64804
  • Maupin, K., Swiler, L., & Swiler, L. (2020). Model Discrepancy Calibration and Propagation Across Experimental Settings [Conference Presenation]. https://doi.org/10.2172/1833158 Publication ID: 71936
  • Loe, J.A., Glusa, C., Boman, E., Yamazaki, I., Rajamanickam, S., & Rajamanickam, S. (2020). Multiprecision Krylov Solvers in Trilinos [Presentation]. https://www.osti.gov/biblio/1829961 Publication ID: 71611
  • Bogle, I., Boman, E., Devine, K., Rajamanickam, S., Slota, G.M., & Slota, G.M. (2020). Distributed Memory Graph Coloring Algorithms for Multiple GPUs [Conference Paper]. Proceedings of IA3 2020: 10th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105511673&origin=inward Publication ID: 71218
  • Bertagna, L., Guba, O., Taylor, M., Foucar, J., Larkin, J., Bradley, A., Rajamanickam, S., Salinger, A., & Salinger, A. (2020). A performance-portable nonhydrostatic atmospheric dycore for the energy exascale earth system model running at cloud-resolving resolutions [Conference Presenation]. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. https://doi.org/10.2172/1830973 Publication ID: 71253
  • Dang, V., Kotulski, J., Rajamanickam, S., & Rajamanickam, S. (2020). ADELUS: A Performance-Portable Dense LU Solver for Distributed-Memory Hardware-Accelerated Systems [Conference Presenation]. https://doi.org/10.2172/1831759 Publication ID: 71778
  • Rajamanickam, S. (2020). AMD Meeting ? Trilinos / Kokkos Kernels requirements [Presentation]. https://www.osti.gov/biblio/1831762 Publication ID: 71783
  • Rajamanickam, S. (2020). Panel:. AI4S Workshop on Artifical Intelligence and Machine Learning for Scientific Applications [Conference Presenation]. https://doi.org/10.2172/1882040 Publication ID: 71788
  • Loe, J.A., Glusa, C., Boman, E., Yamazaki, I., Rajamanickam, S., & Rajamanickam, S. (2020). Multiprecision GMRES in Trilinos packages Belos and Kokkos [Conference Presenation]. https://doi.org/10.2172/1832692 Publication ID: 71889
  • Rajamanickam, S. (2020). FASTMath one slide package summary [Presentation]. https://www.osti.gov/biblio/1833147 Publication ID: 71922
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2020). Mixed-Precision GMRES in Trilinos [Conference Presenation]. https://doi.org/10.2172/1833786 Publication ID: 71988
  • Emery, B., Staid, A., Swiler, L., & Swiler, L. (2020). Sensitivity and Uncertainty Analysis of Generator Failures under Extreme Temperature Scenarios in Power Systems. https://doi.org/10.2172/1808746 Publication ID: 71307
  • Bogle, I., Boman, E., Devine, K., Rajamanickam, S., Slota, G., & Slota, G. (2020). Distributed Graph Coloring on Multiple GPUs [Conference Presenation]. https://doi.org/10.2172/1825832 Publication ID: 71214
  • Rajamanickam, S. (2020). Intersection of Machine Learning and Scientific Simulations: Architectures and Applications perspectives [Conference Presenation]. https://doi.org/10.2172/1826454 Publication ID: 71287
  • Rajamanickam, S. (2020). Panel:. How to broaden compiler/architecture research participants by utilizing modern infrastructures [Conference Presenation]. https://doi.org/10.2172/1830908 Publication ID: 71440
  • Teranishi, K., Dunlavy, D., Myers, J., Barrett, R., & Barrett, R. (2020). SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data [Conference Poster]. 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099384524&origin=inward Publication ID: 70939
  • Myers, J., Dunlavy, D., Teranishi, K., Hollman, D., & Hollman, D. (2020). Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software [Conference Poster]. 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099395134&origin=inward Publication ID: 74785
  • Moore, S., Thompson, A., & Thompson, A. (2020). Solidification Kinetics [Presentation]. https://www.osti.gov/biblio/1822625 Publication ID: 70962
  • Stewart, J., Modine, N., Dingreville, R., & Dingreville, R. (2020). Re-examining the silicon self-interstitial charge states and defect levels: A density functional theory and bounds analysis study. AIP Advances, 10(9). https://doi.org/10.1063/5.0016134 Publication ID: 74461
  • Bogle, I., Boman, E., Devine, K., Rajamanickam, S., Slota, G., & Slota, G. (2020). Distributed Memory Graph Coloring Algorithms for Multiple GPUs [Conference Poster]. https://www.osti.gov/biblio/1820897 Publication ID: 74843
  • Moon, G.E., Ellis, J.A., Sukumaran-Rajam, A., Parthasarathy, S., Sadayappan, P., & Sadayappan, P. (2020). ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization [Conference Poster]. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090404850&origin=inward Publication ID: 74051
  • Tran, A., Wildey, T., Tranchida, J., Thompson, A., & Thompson, A. (2020). Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys. Journal of Chemical Physics, 153(7). https://doi.org/10.1063/5.0015672 Publication ID: 73589
  • Yamazaki, I., Rajamanickam, S., Ellingwood, N., & Ellingwood, N. (2020). Performance Portable Supernode-based Sparse Triangular Solver for Manycore Architectures [Conference Poster]. ACM International Conference Proceeding Series. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090564383&origin=inward Publication ID: 74474
  • Tran, A., Mitchell, J., Swiler, L., Wildey, T., & Wildey, T. (2020). An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics. Acta Materialia, 194, pp. 80-92. https://doi.org/10.1016/j.actamat.2020.04.054 Publication ID: 73364
  • Gulian, M., Swiler, L., Frankel, A., Safta, C., Jakeman, J., & Jakeman, J. (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges [Conference Poster]. https://www.osti.gov/biblio/1814448 Publication ID: 74592
  • Yamazaki, I., Rajamanickam, S., Ellingwood, N., & Ellingwood, N. (2020). Performance Portable Supernode-based Sparse Triangular Solver for Manycore Architecture [Conference Poster]. https://www.osti.gov/biblio/1814123 Publication ID: 74572
  • Bertagna, L., Guba, O., Taylor, M., Foucar, J., Larkin, J., Bradley, A., Rajamanickam, S., Salinger, A., & Salinger, A. (2020). A performance-portable nonhydrostatic atmospheric dycore for the Energy Exascale Earth System Model running at cloud-resolving resolutions [Conference Poster]. https://www.osti.gov/biblio/1818055 Publication ID: 74689
  • DeRosa, S., Finley, P., Finley, M., Beyeler, W., Krofcheck, D., Frazier, C., Swiler, L., Portone, T., Acquesta, E., Austin, P., Levin, D., Taylor, R., Tremba, K., Makvandi, M., Hammer, A., Davis, C., & Davis, C. (2020). COVID-19 Medical Resource Demands [Presentation]. https://www.osti.gov/biblio/1807655 Publication ID: 74013
  • Gulian, M., Swiler, L., Frankel, A., Jakeman, J., Safta, C., & Safta, C. (2020). A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges [Conference Poster]. https://www.osti.gov/biblio/1812282 Publication ID: 74359
  • Thompson, A. (2020). Machine-Learning Potentials: The Unreasonable Effectiveness of Linear Cluster Expansions [Conference Poster]. https://www.osti.gov/biblio/1811808 Publication ID: 74284
  • Dunlavy, D. (2020). Tensor Decompositions for Analyzing Multi-Way Data [Presentation]. https://www.osti.gov/biblio/1809204 Publication ID: 74146
  • Heidbrink, S., Dunlavy, D., Rodhouse, K., & Rodhouse, K. (2020). Multimodal Deep Learning for Flaw Detection in Software Programs [Presentation]. https://www.osti.gov/biblio/1811608 Publication ID: 74247
  • Rajamanickam, S. (2020). Recent experiences withMachine Learning Perspectives fromAlgorithms Architectures and Applications [Presentation]. https://www.osti.gov/biblio/1812175 Publication ID: 74354
  • Ellis, J.A. (2020). Accelerating Multiscale Materials Modeling with Machine Learning [Presentation]. https://www.osti.gov/biblio/1808782 Publication ID: 74105
  • Swiler, L., Portone, T., & Portone, T. (2020). Uncertainty Analysis of a COVID-19 Medical Resource Model [Presentation]. https://www.osti.gov/biblio/1807392 Publication ID: 73743
  • Bisila, J., Dunlavy, D., Gastelum, Z., Ulmer, C., & Ulmer, C. (2020). TOPIC MODELING WITH NATURAL LANGUAGE PROCESSING FOR IDENTIFICATION OF NUCLEAR PROLIFERATION-RELEVANT SCIENTIFIC AND TECHNICAL PUBLICATIONS [Conference Poster]. https://www.osti.gov/biblio/1807413 Publication ID: 73765
  • Myers, J., Dunlavy, D., Teranishi, K., Hollman, D., & Hollman, D. (2020). Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software [Conference Poster]. https://www.osti.gov/biblio/1798424 Publication ID: 73836
  • Teranishi, K., Dunlavy, D., Myers, J., Barrett, R., & Barrett, R. (2020). SparTen: Leveraging Kokkos for On-node Parallelism in a Second-Order Method for Fitting Canonical Polyadic Tensor Models to Poisson Data [Conference Poster]. https://www.osti.gov/biblio/1798425 Publication ID: 73837
  • Bisila, J., Dunlavy, D., Gastelum, Z., Ulmer, C., & Ulmer, C. (2020). TOPIC MODELING WITH NATURAL LANGUAGE PROCESSING FOR IDENTIFICATION OF NUCLEAR PROLIFERATION-RELEVANT SCIENTIFIC AND TECHNICAL PUBLICATIONS [Conference Poster]. https://www.osti.gov/biblio/1805335 Publication ID: 73955
  • Ellis, J.A., Rajamanickam, S., & Rajamanickam, S. (2020). Accelerating Multiscale Materials Modeling with Machine Learning [Presentation]. https://www.osti.gov/biblio/1787726 Publication ID: 73650
  • Ellis, J.A., Rajamanickam, S., & Rajamanickam, S. (2020). Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels [Conference Poster]. https://doi.org/10.1109/HPEC.2019.8916378 Publication ID: 73651
  • Yamazaki, I., Rajamanickam, S., Ellingwood, N., & Ellingwood, N. (2020). Supernode-based Sparse Triangular Solver using Kokkos [Conference Poster]. https://www.osti.gov/biblio/1804644 Publication ID: 73856
  • Ellingwood, N., Rajamanickam, S., & Rajamanickam, S. (2020). Practices and Challenges of Software Development for a Performance Portable Ecosystem [Conference Poster]. https://www.osti.gov/biblio/1805332 Publication ID: 73951
  • Moon, G.E., Ellis, J.A., Sukumaran-Rajam, A., Parthasarathy, S., Sadayappan, P., & Sadayappan, P. (2020). ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization [Conference Poster]. https://www.osti.gov/biblio/1807411 Publication ID: 73763
  • Moon, G.E., Ellis, J.A., Sukumaran-Rajam, A., Parthasarathy, S., Sadayappan, P., & Sadayappan, P. (2020). ALO-NMF: Accelerated Locality-Optimized Non-negative Matrix Factorization [Conference Poster]. https://www.osti.gov/biblio/1798068 Publication ID: 73822
  • Beyeler, W., Frazier, C., Swiler, L., Portone, T., Krofcheck, D., & Krofcheck, D. (2020). Treatment Model Design and Use [Presentation]. https://www.osti.gov/biblio/1783073 Publication ID: 73474
  • Swiler, L., Portone, T., Beyeler, W., & Beyeler, W. (2020). Uncertainty analysis of Resource Demand Model for Covid-19. https://doi.org/10.2172/1630395 Publication ID: 73459
  • Acer, S., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2020). SPHYNX: Spectral partitioning for HYbrid and aXelerator-enabled systems [Conference Poster]. Proceedings – 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091572985&origin=inward Publication ID: 73087
  • Moon, G.E., Rajamanickam, S., Krishna, T., Kwon, H., Chatarasi, P., Qin, E., & Qin, E. (2020). Utilizing Spatial Accelerators for Machine Learning and Linear Algebra Kernels [Conference Poster]. https://www.osti.gov/biblio/1782497 Publication ID: 73425
  • Qin, E., Jeong, G., Won, W., Kao, S., Kwon, H., Srinivasan, S., Das, D., Moon, G.E., Rajamanickam, S., Krishna, T., & Krishna, T. (2020). MINT: Microarchitecture for Efficient and Interchangeable CompressioN Formats on Tensor Algebra [Conference Poster]. https://www.osti.gov/biblio/1782675 Publication ID: 73435
  • Acer, S., Boman, E., Rajamanickam, S., & Rajamanickam, S. (2020). SPHYNX: Spectral Partitioning for HYbrid aNd aXelerator-based systems [Conference Poster]. https://www.osti.gov/biblio/1783066 Publication ID: 73470
  • Kotulski, J., Dang, V., Rajamanickam, S., & Rajamanickam, S. (2020). ADELUS: A Performance-Portable Dense LU Solver for Distributed-Memory Hardware-Accelerated Systems [Conference Poster]. https://www.osti.gov/biblio/1783676 Publication ID: 73584
  • Thompson, A., Wood, M., Cangi, A., Desjarlais, M., Tranchida, J., & Tranchida, J. (2020). Improving the accuracy of spin-lattice simulations with machine-learning interatomic potentials [Conference Poster]. https://www.osti.gov/biblio/1778150 Publication ID: 73267
  • Ang, J., Sweeney, C., Wolf, M., Ellis, J.A., Ghosh, S., Kagawa, A., Huang, Y., Rajamanickam, S., Ramakrishnaiah, V., Schram, M., Yoo, S., & Yoo, S. (2020). ECP Report: Update on Proxy Applications and Vendor Interactions. https://doi.org/10.2172/1608914 Publication ID: 73225
  • Maupin, K., Swiler, L., & Swiler, L. (2020). Calibration Propagation and Validation of Model Discrepancy Across Experimental Settings [Conference Poster]. https://www.osti.gov/biblio/1769563 Publication ID: 72925
  • Wood, M., Cusentino, M., Thompson, A., & Thompson, A. (2020). Scale-Bridging From DFT to MD with Machine Learning [Conference Poster]. https://www.osti.gov/biblio/1766923 Publication ID: 72603
  • Cusentino, M., Wood, M., Thompson, A., & Thompson, A. (2020). Molecular Dynamics Simulations of Mixed Materials in Tungsten [Conference Poster]. https://www.osti.gov/biblio/1766750 Publication ID: 72638
  • Brown, J., Kittell, D.E., Wood, M., Thompson, A., Bolintineanu, D., & Bolintineanu, D. (2020). Multiscale modeling to study effects of microstructure in shocked hexanitrostilbene [Conference Poster]. https://www.osti.gov/biblio/1767897 Publication ID: 72758
  • Wood, M., Plimpton, S.J., Thompson, A., Perez, D., Niklasson, A., & Niklasson, A. (2020). A Path to the Exascale for Atomistic Simulations with Improved Accuracy Length and Time Scales [Conference Poster]. https://www.osti.gov/biblio/1783582 Publication ID: 72854
  • Thompson, A. (2020). Predictive Atomistic Simulations of Materials using SNAP Data-Driven Potentials [Conference Poster]. https://www.osti.gov/biblio/1783594 Publication ID: 72866
  • Teranishi, K., Hollman, D., Myers, J., Dunlavy, D., & Dunlavy, D. (2020). Performance and Parallelization of CP-Alternate Poisson Regression Sparse Tensor Decomposition [Conference Poster]. https://www.osti.gov/biblio/1766745 Publication ID: 72606
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May 9, 2023