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

  • Goff, J.M., Sievers, C., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2024). Permutation-adapted complete and independent basis for atomic cluster expansion descriptors. Journal of Computational Physics. https://doi.org/10.2172/1879613 Publication ID: 80036
  • Fiedler, L., Modine, N.A., Schmerler, S., Vogel, D.J., Popoola, G.A., Thompson, A.P., Rajamanickam, S., Cangi, A., & Cangi, A. (2023). Predicting electronic structures at any length scale with machine learning. npj Computational Materials, 9(1). https://doi.org/10.1038/s41524-023-01070-z Publication ID: 122968
  • Cusentino, M.A., Bachman, W.B., McCarthy, M.J., Thompson, A.P., Wood, M.A., & Wood, M.A. (2023). Dynamic formation of preferentially lattice oriented, self trapped hydrogen clusters. Materials Research Express (Online), 10(10). https://doi.org/10.1088/2053-1591/acfae7 Publication ID: 122404
  • Dunlavy, D.M., Lehoucq, R.B., Lopez, O.F., & Lopez, O.F. (2023). Zero-truncated Poisson regression for sparse multiway count data corrupted by false zeros. Information and Inference, 12(3), pp. 1573-1611. https://doi.org/10.1093/imaiai/iaad016 Publication ID: 106884
  • Dunlavy, D.M., Phipps, E.T., Kolla, H., Shadid, J.N., Phillips, E., & Phillips, E. (2023). Constrained Tucker Decompositions and Conservation Principles for Direct Numerical Simulation Data Compression [Conference Presenation]. https://doi.org/10.2172/2430382 Publication ID: 125324
  • Myers, J.M., Dunlavy, D.M., & Dunlavy, D.M. (2023). Recent Improvements in CP Poisson Tensor Algorithms [Conference Presenation]. https://doi.org/10.2172/2430429 Publication ID: 125476
  • Nikolov, S.V., Carpenter, J.H., Wood, M.A., Tranchida, J., Cochrane, K.R., Modine, N.A., Thompson, A.P., Cangi, A., Ramakrishna, K., Rohskopf, A.D., & Rohskopf, A.D. (2023). Leveraging Data Driven Frameworks to Build Transferable Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/2430436 Publication ID: 125500
  • Bachman, W.B., Cusentino, M.A., McCarthy, M.J., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Developing machine learned potentials for high temperature applications [Conference Presenation]. https://doi.org/10.2172/2430457 Publication ID: 125572
  • Loe, J.A., Boman, E.G., Espinoza, H.J.D., Glusa, C., Harper, G.B., Higgins, A.J., Rajamanickam, S., Siefert, C., Switzer, H.M., Szyld, D., Tuminaro, R.S., Yamazaki, I., & Yamazaki, I. (2023). New Linear Solvers Features and Improvements in Trilinos [Conference Presenation]. https://doi.org/10.2172/2463061 Publication ID: 131756
  • Bachman, W.B., Rohskopf, A.D., Goff, J.M., McCarthy, M.J., Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Recent Developments in Machine Learning Interatomic Potentials for Molecular Dynamics [Conference Presenation]. https://doi.org/10.2172/2430609 Publication ID: 126088
  • Mitchell, J.A., Abdeljawad, F., Battaile, C., Garcia-Cardona, C., Holm, E.A., Homer, E.R., Madison, J., Rodgers, T., Thompson, A.P., Tikare, V., Webb, E., Plimpton, S.J., & Plimpton, S.J. (2023). Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing. Modelling and Simulation in Materials Science and Engineering, 31(5). https://doi.org/10.1088/1361-651x/accc4b Publication ID: 106876
  • Modine, N.A., Lee, S.R., & Lee, S.R. (2023). Insight into the Role of Excited States in Carrier Capture by Semiconductor Defects [Conference Poster]. https://doi.org/10.2172/2430923 Publication ID: 127176
  • Myers, J.M., Dunlavy, D.M., & Dunlavy, D.M. (2023). Hybrid Methods for Tensor Decompositions that Leverage Stochastic and Deterministic Optimization [Conference Presenation]. https://doi.org/10.2172/2431218 Publication ID: 128236
  • Nichol, J.J., Weylandt, M., Smith, M.A., Swiler, L.P., & Swiler, L.P. (2023). Benchmarking the PCMCI Causal Discovery Algorithm for Spatiotemporal Systems. https://doi.org/10.2172/1991387 Publication ID: 106984
  • Geronimo Anderson, S.I., Dunlavy, D.M., & Dunlavy, D.M. (2023). Computing Sparse Tensor Decompositions via Chapel and C++/MPI Interoperability without Intermediate I/O [Conference Paper]. https://www.osti.gov/biblio/2431621 Publication ID: 129564
  • Dunlavy, D.M., Kolla, H., Phipps, E.T., Shadid, J.N., Phillips, E., & Phillips, E. (2023). Low-Rank Tensor Decompositions with Nonlinear Constraints for Conserving Quantities of Interest in Numerical Simulation Data Modeling [Conference Presenation]. https://doi.org/10.2172/2431068 Publication ID: 127712
  • Cusentino, M.A., Bachman, W.B., McCarthy, M.J., Goff, J.M., Rohskopf, A.D., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Atomistic modeling of plasma material interactions using SNAP machine learned interatomic potentials [Conference Presenation]. https://doi.org/10.2172/2431239 Publication ID: 128312
  • Cusentino, M.A., McCarthy, M.J., Bachman, W.B., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Development of Machine Learned Interatomic Potentials for Modeling the Effect of Mixed Material Layers on Hydrogen Retention [Conference Poster]. https://doi.org/10.2172/2431214 Publication ID: 128224
  • Thompson, A.P., Clavier, G., & Clavier, G. (2023). Computation of the thermal elastic constants for arbitrary manybody potentials in LAMMPS using the stress-fluctuation formalism. Computer Physics Communications, 286(2023). https://doi.org/10.1016/j.cpc.2023.108674 Publication ID: 106716
  • Liegeois, K., Rajamanickam, S., Berger-Vergiat, L., & Berger-Vergiat, L. (2023). Performance Portable Batched Sparse Linear Solvers. IEEE Transactions on Parallel and Distributed Systems, 34(5), pp. 1524-1535. https://doi.org/10.1109/tpds.2023.3249110 Publication ID: 106780
  • Yamazaki, I., Rajamanickam, S., Heinlein, A., & Heinlein, A. (2023). An Experimental Study of Two-Level Schwarz Domain Decomposition Preconditioners on GPUs [Conference Presenation]. https://doi.org/10.2172/2431213 Publication ID: 128220
  • Hart, J.L., Gulian, M., Manickam, I., Swiler, L.P., & Swiler, L.P. (2023). Solving inverse problems via neural network flow map approximation [Conference Presenation]. https://doi.org/10.2172/2431580 Publication ID: 129424
  • Gilbert, M., Madduri, K., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2023). Jet: Multilevel Partitioning on GPUs [Conference Paper]. https://www.osti.gov/biblio/2432093 Publication ID: 131216
  • Nikolov, S.V., Nieves, P., Thompson, A.P., Wood, M.A., Tranchida, J., & Tranchida, J. (2023). Temperature dependence of magnetic anisotropy and magnetoelasticity from classical spin-lattice calculations. Physical Review. B, 107(9). https://doi.org/10.1103/physrevb.107.094426 Publication ID: 123424
  • Bachman, W.B., Cusentino, M.A., McCarthy, M.J., Tranchida, J., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Machine learned interatomic potential for dispersion strengthened plasma facing components. Journal of Chemical Physics, 158(11). https://doi.org/10.1063/5.0135269 Publication ID: 123584
  • McCarthy, M.J., Startt, J.K., Dingreville, R.P.M., Thompson, A.P., Wood, M.A., & Wood, M.A. (2023). Investigating the influence of local composition on properties in complex alloys using machine learned interatomic potentials [Conference Presenation]. https://doi.org/10.2172/2431768 Publication ID: 130080
  • Montes de Zapiain, D., Wood, M.A., Sema, D., Thompson, A.P., & Thompson, A.P. (2023). Optimal Development of Transferable Machine Learning Interatomic Potentials using Active Learning [Poster]. https://doi.org/10.2172/2431809 Publication ID: 130236
  • Naugle, A.B., Krofcheck, D.J., Warrender, C., Lakkaraju, K., Swiler, L.P., Verzi, S.J., 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
  • Thompson, A.P. (2023). The LAMMPS particle simulation package: Bringing together innovative physics models, machine-learning interatomic potentials, and extreme-scale computing resources [Conference Presenation]. https://doi.org/10.2172/2431779 Publication ID: 130120
  • Llosa, C., Dunlavy, D.M., & Dunlavy, D.M. (2023). Poisson-response Tensor-on-Tensor Regression [Poster]. https://doi.org/10.2172/2432049 Publication ID: 131076
  • Bachman, W.B., Cusentino, M.A., McCarthy, M.J., Tranchida, J., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2023). Machine-learned Interatomic Potential Development for H Trapping in ZrC Strengthened W [Conference Presenation]. https://doi.org/10.2172/2431685 Publication ID: 129784
  • Nikolov, S.V., Tranchida, J., Thompson, A.P., Cangi, A., Ramakrishna, K., Rohskopf, A.D., Wood, M.A., & Wood, M.A. (2023). Examining the Alpha-Epsilon Transition in Iron Using Molecular-spin Dynamics [Conference Presenation]. https://doi.org/10.2172/2431651 Publication ID: 129668
  • Dunlavy, D.M., Kolla, H., Phipps, E.T., & Phipps, E.T. (2023). Conserving Quantities of Interest in Low-Rank Tensor Decompositions of Numerical Simulation Data [Poster]. https://doi.org/10.2172/2431902 Publication ID: 130600
  • Myers, J.M., Dunlavy, D.M., & Dunlavy, D.M. (2023). Tensor Decompositions using Stochastic and Deterministic Optimization [Poster]. https://doi.org/10.2172/2432050 Publication ID: 131080
  • Yamazaki, I., Loe, J.A., Glusa, C., Rajamanickam, S., Luszczek, P., Dongarra, J., & Dongarra, J. (2023). High-Performance GMRES Multi-Precision Benchmark [Conference Presenation]. https://doi.org/10.2172/2431881 Publication ID: 130520
  • McCarthy, M.J., Startt, J.K., Dingreville, R.P.M., Thompson, A.P., Wood, M.A., & Wood, M.A. (2023). Training machine-learned interatomic potentials for chemical complexity – application to refractory CCAs [Conference Presenation]. https://doi.org/10.2172/2431658 Publication ID: 129696
  • Lopez, O.F., Dunlavy, D.M., Lehoucq, R.B., & Lehoucq, R.B. (2023). Low-Rank Models for Tensor Count Data Corrupted by False Zeros [Conference Poster]. https://doi.org/10.2172/2431969 Publication ID: 130816
  • Yamazaki, I., Heinlein, A., Rajamanickam, S., & Rajamanickam, S. (2023). An Experimental Study of Two-level Schwarz Domain-Decomposition Preconditioners on GPUs [Conference Paper]. Proceedings – 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023. https://doi.org/10.1109/IPDPS54959.2023.00073 Publication ID: 130440
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (2023). Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository. Nuclear Technology, 209(9), pp. 1295-1318. https://doi.org/10.1080/00295450.2023.2197666 Publication ID: 123668
  • Naugle, A.B., Verzi, S.J., Lakkaraju, K., Swiler, L.P., 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
  • Montes de Zapiain, D., Wood, M.A., Lubbers, N., Pereyra, C.Z., Thompson, A.P., Perez, D., & Perez, D. (2022). Training data selection for accuracy and transferability of interatomic potentials. npj Computational Materials, 8(1). https://doi.org/10.1038/s41524-022-00872-x Publication ID: 106592
  • Kononov, A., Lee, C.W., dos Santos, T.P., Robinson, B., Yao, Y., Yao, Y., Andrade, X., Baczewski, A.D., Constantinescu, E., Correa, A.A., Kanai, Y., Modine, N.A., 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
  • Portone, T., Mariner, P.E., Bachman, W.B., Basurto, E., Leone, R.C., Stein, E., Swiler, L.P., & Swiler, L.P. (2022). Sensitivity analysis for deep geologic repository simulations in crystalline rock [Conference Presenation]. https://doi.org/10.2172/2006253 Publication ID: 121724
  • Bull, D.L., Peterson, K., Tezaur, I.K., Shand, L., Swiler, L.P., & Swiler, L.P. (2022). CLDERA. AGU Fall 2022 [Conference Poster]. https://doi.org/10.2172/2006286 Publication ID: 121856
  • Moore, S.G., Wood, M.A., Cochrane, K.R., Thompson, A.P., & Thompson, A.P. (2022). Extreme-Scale Atomistic Simulations of Molten Metal Expansion [Conference Presenation]. https://doi.org/10.2172/2006153 Publication ID: 121328
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Presenation]. https://doi.org/10.2172/2006027 Publication ID: 120840
  • Brooks, D.M., Swiler, L.P., Stein, E., Mariner, P.E., Basurto, E., Portone, T., Eckert, A., Leone, R.C., & Leone, R.C. (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
  • Portone, T., Geraci, G., Swiler, L.P., Eldred, M., & Eldred, M. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Presenation]. https://doi.org/10.2172/2005951 Publication ID: 120548
  • Yamazaki, I., Loe, J.A., Glusa, C., Rajamanickam, S., Luszczek, P., Dongarra, J., & Dongarra, J. (2022). High-Performance GMRES Multi-Precision Benchmark [Conference Presenation]. https://doi.org/10.2172/2006086 Publication ID: 121072
  • Rajamanickam, S., Eydenberg, M.S., Ho, Y., Liu, C., Zhang, L., Zhou, K., Sun, J.G., Chen, E., Deng, A., Wang, M., & Wang, M. (2022). Accelerating Selected DOE Machine Learning Workloads on SambaNova Systems [Conference Presenation]. https://doi.org/10.2172/2006163 Publication ID: 121368
  • Fiedler, L., Modine, N.A., Schmerler, S., Vogel, D.J., Popoola, G.A., Thompson, A.P., 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
  • McCarthy, M.J., Startt, J.K., Dingreville, R.P.M., Thompson, A.P., Wood, M.A., & Wood, M.A. (2022). Exploring refractory complex concentrated alloy behavior in the fusion reactor environment with a machine-learned interatomic potential [Conference Presenation]. https://doi.org/10.2172/2005320 Publication ID: 118728
  • Swiler, L.P. (2022). Scientific Machine Learning needs in climate attribution: an application example [Conference Presenation]. https://doi.org/10.2172/2005330 Publication ID: 118768
  • Harvey, E.C., Milewicz, R.M., Trott, C.R., Berger-Vergiat, L., Rajamanickam, S., & Rajamanickam, S. (2022). Half-Precision Scalar Support in Kokkos and Kokkos Kernels: An Engineering Study and Experience Report [Conference Presenation]. https://doi.org/10.2172/2005442 Publication ID: 119208
  • Bachman, W.B., Cusentino, M.A., McCarthy, M.J., Tranchida, J., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Machine Learned Interatomic Potential Development of W-ZrC for Fusion Divertor Microstructure and Thermomechanical Properties [Conference Presenation]. https://doi.org/10.2172/2005312 Publication ID: 118696
  • Cusentino, M.A., McCarthy, M.J., Bachman, W.B., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Molecular Dynamics Modeling of Hydrogen and Nitrogen Implantation in Tungsten Using Machine Learned Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/2005386 Publication ID: 118984
  • Dunlavy, D.M., Geronimo Anderson, S.I., & Geronimo Anderson, S.I. (2022). Chapel/C++ Interoperability: Enabling Large-Scale Data Science without Costly I/O [Conference Presenation]. https://doi.org/10.2172/2005698 Publication ID: 119928
  • Moore, S.G., Wood, M.A., Cochrane, K.R., Thompson, A.P., & Thompson, A.P. (2022). Extreme-Scale Atomistic Simulations of Molten Metal Expansion [Conference Poster]. https://doi.org/10.2172/2005326 Publication ID: 118752
  • 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.E., Debusschere, B.J., Fukuyama, D.E., Harvey, J.A., Laforce, T., Leone, R.C., Bachman, W.B., Swiler, L.P., Taconi, A., & Taconi, A. (2022). GDSA Framework Development and Process Model Integration FY2022. https://doi.org/10.2172/1893995 Publication ID: 80378
  • Dunlavy, D.M., Ballard, G., Kolda, T.G., & Kolda, T.G. (2022). Generalized CP (GCP) Tensor Decompositions [Conference Presenation]. https://doi.org/10.2172/2004702 Publication ID: 117640
  • Thompson, A.P., Clavier, G., & Clavier, G. (2022). A General Method for Calculating Local Stress and Elastic Constants for Arbitrary Many-body Interaction Potentials in LAMMPS [Conference Presenation]. https://doi.org/10.2172/2005287 Publication ID: 118600
  • Modine, N.A., Stephens, J.A., Swiler, L.P., Thompson, A.P., Vogel, D.J., 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
  • Rajamanickam, S. (2022). Computational Challenges in the development of a surrogate model for Density Functional Theory calculations [Conference Presenation]. https://doi.org/10.2172/2004729 Publication ID: 117748
  • Thompson, A.P. (2022). SNAP and Beyond: Machine Learning Interatomic Potentials in LAMMPS [Conference Presenation]. https://doi.org/10.2172/2004681 Publication ID: 117556
  • Swiler, L.P., Basurto, E., Brooks, D.M., Eckert, A., Leone, R.C., Mariner, P.E., Portone, T., Bachman, W.B., & Bachman, W.B. (2022). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2022). https://doi.org/10.2172/1884909 Publication ID: 80086
  • Thorpe, J., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., Debusschere, B.J., & Debusschere, B.J. (2022). Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Presenation]. ACM International Conference Proceeding Series. https://doi.org/10.2172/2004219 Publication ID: 116112
  • Naugle, A.B., Swiler, L.P., Lakkaraju, K., Verzi, S.J., 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
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Paper]. https://www.osti.gov/biblio/2004284 Publication ID: 116352
  • Portone, T., Swiler, L.P., Geraci, G., Eldred, M., & Eldred, M. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Paper]. https://www.osti.gov/biblio/2004288 Publication ID: 116368
  • Lane, J.M.D., Cusentino, M.A., Nebgen, B., Barros, K.M., Shimanek, J.D., Allen, A., Thompson, A.P., Fensin, S.J., & Fensin, S.J. (2022). Molecular Dynamics of High Pressure Tin Phases I: Strength and deformation evaluations of empirical potentials [Slides] [Conference Presenation]. https://doi.org/10.2172/2004049 Publication ID: 115452
  • Thorpe, J., Swiler, L.P., Tarman, T.D., Hanson, S.T., Cruz, G.J., Debusschere, B.J., Rollins, T., & Rollins, T. (2022). Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Paper]. https://www.osti.gov/biblio/2003842 Publication ID: 114652
  • Rohskopf, A.D., Sievers, C., McCarthy, M.J., Goff, J.M., Bachman, W.B., Thompson, A.P., Wood, M.A., & Wood, M.A. (2022). FitSNAP: Machine Learned Potentials for LAMMPS [Conference Poster]. https://doi.org/10.2172/2004206 Publication ID: 116060
  • Hart, J.L., Gulian, M., Manickam, I., Swiler, L.P., & Swiler, L.P. (2022). Facilitating Atmospheric Source Inversion via Operator Regression [Conference Presenation]. https://doi.org/10.2172/2003973 Publication ID: 115156
  • Bachman, W.B., Cusentino, M.A., McCarthy, M.J., Tranchida, J., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Machine-Learned Interatomic Potential Development for W-ZrC for Nuclear Fusion [Conference Poster]. https://doi.org/10.2172/2004135 Publication ID: 115792
  • Cusentino, M.A., Nebgen, B., Barros, K.M., Shimanek, J.D., Allen, A., Thompson, A.P., Fensin, S.J., Lane, J.M.D., & Lane, J.M.D. (2022). Molecular Dynamics of High Pressure Tin Phases II: Machine Learned Interatomic Potential Development [Conference Presenation]. https://doi.org/10.2172/2003977 Publication ID: 115172
  • Goff, J.M., Wood, M.A., Thompson, A.P., Sievers, C., & Sievers, C. (2022). Permutation-Adapted Atomic Cluster Expansion Models [Conference Poster]. https://doi.org/10.2172/2004172 Publication ID: 115924
  • McCarthy, M.J., Thompson, A.P., Wood, M.A., & Wood, M.A. (2022). Building a new generation of multiscale materials models with machine-learned interatomic potentials [Conference Presenation]. https://doi.org/10.2172/2003841 Publication ID: 114648
  • Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Arguello, B., Geraci, G., Debusschere, B.J., Hanson, S.T., Outkin, A.V., Thorpe, J., Hart, W.E., Sahakian, M., Gabert, K.G., Glatter, C., Johnson, E.S., Punla-Green, S., & Punla-Green, S. (2022). Uncertainty Quantification within SECURE LDRD [Presentation]. https://www.osti.gov/biblio/2003628 Publication ID: 113820
  • Peterson, K., Bull, D.L., Tezaur, I.K., Shand, L., Swiler, L.P., & Swiler, L.P. (2022). CLDERA: A Novel Foundational Approach for Attributing Climate Impacts [Conference Presenation]. https://doi.org/10.2172/2003488 Publication ID: 113276
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Paper]. https://www.osti.gov/biblio/2003687 Publication ID: 114044
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2022). Polynomial Preconditioning GMRES with Mixed Precisions [Conference Presenation]. https://doi.org/10.2172/2003462 Publication ID: 113172
  • Bachman, W.B., Tranchida, J., Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Development of SNAP potential for ZrC strengthened W [Conference Presenation]. https://doi.org/10.2172/2003450 Publication ID: 113124
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., Morgan, R., & Morgan, R. (2022). Polynomial Preconditioning GMRES with Mixed Precisions [Conference Presenation]. https://www.osti.gov/biblio/2003557 Publication ID: 113544
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Presenation]. https://doi.org/10.2172/2003735 Publication ID: 114232
  • Lopez, O., Dunlavy, D.M., Lehoucq, R.B., & Lehoucq, R.B. (2022). Zero-Truncated Poisson Regression for Multiway Count Data [Conference Poster]. https://doi.org/10.2172/2003556 Publication ID: 113540
  • Gulian, M., Hart, J.L., Manickam, I., Swiler, L.P., & Swiler, L.P. (2022). Facilitating Atmospheric Source Inversion via Deep Operator Network Surrogates [Conference Presenation]. https://doi.org/10.2172/2003586 Publication ID: 113660
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Paper]. https://doi.org/10.56952/ARMA-DFNE-22-0007 Publication ID: 114248
  • Dunlavy, D.M., Lehoucq, R.B., Lopez, O., & Lopez, O. (2022). Low-Rank Tensor Decompositions for Large Sparse Count Data [Conference Presenation]. https://doi.org/10.2172/2003472 Publication ID: 113212
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2022). Mixed Precision Strategies for GMRES in TrilinosJennifer [Presentation]. https://www.osti.gov/biblio/2003637 Publication ID: 113852
  • Wong, C., Kolasinski, R., Whaley, J., Cusentino, M.A., Wood, M.A., Wirth, B., Thompson, A.P., & Thompson, A.P. (2022). How nitrogen affects hydrogen adsorption on tungsten surfaces [Conference Poster]. https://doi.org/10.2172/2003479 Publication ID: 113240
  • Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Development of SNAP Potentials for Molecular Dynamics Modeling of Hydrogen and Nitrogen Interactions in Tungsten [Conference Presenation]. https://doi.org/10.2172/2003436 Publication ID: 113072
  • Modine, N.A., Fiedler, L., Vogel, D.J., Thompson, A.P., Ellis, A., Stephens, J.A., Popoola, G., Cangi, A., Rajamanickam, S., & Rajamanickam, S. (2022). A machine learning surrogate for density functional theory based on the local density of state [Conference Presenation]. https://doi.org/10.2172/2003393 Publication ID: 112904
  • Berger-Vergiat, L., Rajamanickam, S., Dang, V., Kelley, B., Ellingwood, N.D., Loe, J.A., Harvey, E.C., Pearson, C., Foucar, J.G., Liegeois, K., & Liegeois, K. (2022). Kokkos Kernels (Sake project) [Conference Poster]. https://doi.org/10.2172/2003182 Publication ID: 112132
  • Liegeois, K., Rajamanickam, S., Berger-Vergiat, L., & Berger-Vergiat, L. (2022). Performance portable batched sparse linear solvers in Kokkos Kernels [Conference Presenation]. https://doi.org/10.2172/2003169 Publication ID: 112080
  • Berger-Vergiat, L., Rajamanickam, S., Loe, J.A., Kelley, B., Harvey, E.C., Foucar, J.G., Ellingwood, N.D., Dang, V., Liegeois, K., Pearson, C., & Pearson, C. (2022). Kokkos Kernels Math Library [Conference Presenation]. https://doi.org/10.2172/2003140 Publication ID: 111976
  • Myers, J.M., Dunlavy, D.M., & Dunlavy, D.M. (2022). A Hybrid Method for Tensor Decompositions that Leverages Stochastic and Deterministic Optimization. https://doi.org/10.2172/1865529 Publication ID: 80700
  • Naugle, A.B., Russell, A., Lakkaraju, K., Swiler, L.P., Verzi, S.J., 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
  • Boman, E.G., Rajamanickam, S., Teranishi, K., Yamazaki, I., & Yamazaki, I. (2022). Trilinos for Exascale [Presentation]. https://www.osti.gov/biblio/2002504 Publication ID: 110932
  • Swiler, L.P., Portone, T., Mariner, P.E., Leone, R., Brooks, D.M., Seidl, D.T., Debusschere, B.J., Berg, T., & Berg, T. (2022). Use of a machine learning model for a constitutive chemistry model within a groundwater flow and transport application modeling nuclear fuel degradation in a waste repository [Conference Presenation]. https://doi.org/10.2172/2002228 Publication ID: 109876
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Paper]. https://doi.org/10.56952/ARMA-DFNE-22-0007 Publication ID: 109972
  • 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
  • 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. https://doi.org/10.2172/1808019 Publication ID: 78992
  • Mariner, P.E., Basurto, E., Brooks, D.M., Leone, R.C., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Advanced Use of Tracers in Repository Performance Assessment Modeling [Conference Paper]. https://www.osti.gov/biblio/2002123 Publication ID: 109468
  • Thorpe, J., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., Debusschere, B.J., & Debusschere, B.J. (2022). WiP: Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Paper]. https://www.osti.gov/biblio/2002055 Publication ID: 109200
  • Kelley, B., Rajamanickam, S., & Rajamanickam, S. (2022). Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening [Conference Paper]. https://doi.org/10.1109/IPDPS53621.2022.00035 Publication ID: 109012
  • Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Molecular Dynamics Simulations of Hydrogen and Nitrogen Implantation in Tungsten [Conference Poster]. https://doi.org/10.2172/2001960 Publication ID: 108840
  • Wood, M.A., Cusentino, M.A., Oleynik, I., Thompson, A.P., & Thompson, A.P. (2022). Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials [Conference Presenation]. https://doi.org/10.2172/2001917 Publication ID: 108680
  • Thorpe, J., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., Debusschere, B.J., & Debusschere, B.J. (2022). WiP: Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Presenation]. https://doi.org/10.2172/2002078 Publication ID: 109288
  • Wood, M.A., Sievers, C., Perez, D., Lubbers, N., Thompson, A.P., & Thompson, A.P. (2022). Scalable Solutions for Training Machine Learned Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/2001910 Publication ID: 108656
  • Swiler, L.P. (2022). Metamodelling sensitivity approaches versus regression and graphical methods on the basis of Geologic Cases: An International Collaboration [Conference Presenation]. https://doi.org/10.2172/2001987 Publication ID: 108948
  • Fiedler, L., Hoffmann, N., Mohammed, P., Popoola, G.A., Yovell, T., Oles, V., Ellis, J.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
  • Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Development of SNAP Interatomic Potentials for Gas-Metal Interactions for Fusion Energy Materials [Conference Presenation]. https://doi.org/10.2172/2001794 Publication ID: 108256
  • Myers, J.M., Dunlavy, D.M., & Dunlavy, D.M. (2022). Cyclic GCP-CPAPR Hybrid [Conference Presenation]. https://doi.org/10.2172/2001703 Publication ID: 107900
  • Wood, M.A., Sievers, C., Perez, D., Lubbers, N., Thompson, A.P., & Thompson, A.P. (2022). Scalable Solutions for Training Machine Learned Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/2001823 Publication ID: 108340
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2022). Mixed Precision Strategies for GMRES [Conference Presenation]. https://doi.org/10.2172/2001827 Publication ID: 108352
  • Liegeois, K., Rajamanickam, S., Berger-Vergiat, L., & Berger-Vergiat, L. (2022). Performance portable batched sparse linear solvers in Kokkos kernels [Conference Presenation]. https://doi.org/10.2172/2001799 Publication ID: 108272
  • Yamazaki, I., Heinlein, A., Rajamanickam, S., & Rajamanickam, S. (2022). Accelerating FROSch preconditioner using GPUs [Conference Presenation]. https://doi.org/10.2172/2001850 Publication ID: 108428
  • Yasar, A., Rajamanickam, S., Berry, J., 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
  • Bachman, W.B., Tranchida, J., Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Machine Learned Interatomic Potential Development for W-ZrC [Conference Presenation]. https://doi.org/10.2172/2001815 Publication ID: 108312
  • Nikolov, S.V., Tranchida, J., Thompson, A.P., Wood, M.A., & Wood, M.A. (2022). Data-driven Magnetic Materials Modeling; Advances in Classical Molecular Dynamics [Conference Presenation]. https://doi.org/10.2172/2001816 Publication ID: 108316
  • Goff, J.M., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2022). Training Atomic Cluster Expansion Potentials using LAMMPS and FitSNAP [Conference Presenation]. https://doi.org/10.2172/2001842 Publication ID: 108404
  • Garg, R., Qin, E., Martinez, F.M., Guirado, R., Jain, A., Abadal, S., Abellan, J.L., Acacio, M.E., Alarcon, E., Rajamanickam, S., Krishna, T., & Krishna, T. (2022). Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators [Conference Paper]. https://www.osti.gov/biblio/2001845 Publication ID: 108408
  • Lopez, O.F., Lehoucq, R.B., Dunlavy, D.M., & Dunlavy, D.M. (2022). Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data. https://doi.org/10.2172/1841834 Publication ID: 79989
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Presenation]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://doi.org/10.2172/2006048 Publication ID: 120920
  • Portone, T., Eldred, M., Geraci, G., Swiler, L.P., & Swiler, L.P. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181554658&origin=inward Publication ID: 114276
  • Harvey, E.C., Milewicz, R.M., Trott, C.R., Berger-Vergiat, L., Rajamanickam, S., & Rajamanickam, S. (2022). Half-Precision Scalar Support in Kokkos and Kokkos Kernels: An Engineering Study and Experience Report [Conference Proceeding]. Proceedings – 2022 IEEE 18th International Conference on e-Science, eScience 2022. https://doi.org/10.1109/eScience55777.2022.00095 Publication ID: 116504
  • Kelley, B., Rajamanickam, S., & Rajamanickam, S. (2022). Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening [Conference Presenation]. Proceedings – 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022. https://doi.org/10.2172/2003043 Publication ID: 111628
  • Brooks, D.M., Swiler, L.P., Mariner, P.E., Portone, T., Basurto, E., Leone, R.C., & Leone, R.C. (2022). Sensitivity and Uncertainty Analysis of FMD Model Choice for a Generic Crystalline Repository [Conference Presenation]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://doi.org/10.2172/2005997 Publication ID: 120724
  • Brooks, D.M., Swiler, L.P., Mariner, P.E., Portone, T., Basurto, E., Leone, R.C., & Leone, R.C. (2022). Sensitivity and Uncertainty Analysis of FMD Model Choice for a Generic Crystalline Repository [Conference Proceeding]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181554518&origin=inward Publication ID: 116360
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward Publication ID: 116388
  • Debusschere, B.J., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., Mariner, P.E., & Mariner, P.E. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward Publication ID: 114300
  • 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
  • Yamazaki, I., Glusa, C., Loe, J.A., Luszczek, P., Rajamanickam, S., Dongarra, J., & Dongarra, J. (2022). High-Performance GMRES Multi-Precision Benchmark: Design, Performance, and Challenges [Conference Paper]. Proceedings of PMBS 2022: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis. https://doi.org/10.1109/PMBS56514.2022.00015 Publication ID: 119936
  • Gulian, M., Frankel, A., Swiler, L.P., & Swiler, L.P. (2022). Gaussian process regression constrained by boundary value problems. Computer Methods in Applied Mechanics and Engineering, 388. https://doi.org/10.1016/j.cma.2021.114117 Publication ID: 75489
  • Lysogorskiy, Y., van der Oord, V., Bochkarev, A., Menon, S., Rinaldi, M., Hammerschmidt, T., Mrovec, M., Thompson, A.P., Csanyi, 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
  • Rajamanickam, S. (2021). Can Scientific Software Development Use the Outsourcing Model Successfully? [Conference Presenation]. https://doi.org/10.2172/1908776 Publication ID: 77136
  • Dunlavy, D.M., Chew, P.A., & Chew, P.A. (2021). Document Retrieval and Ranking using Similarity Graph Mean Hitting Times. https://doi.org/10.2172/1835671 Publication ID: 77129
  • Swiler, L.P. (2021). Uncertainty Quantification (UQ) and Sensitivity Analysis (SA) in GDSA [Presentation]. https://www.osti.gov/biblio/1901835 Publication ID: 77036
  • Rajamanickam, S., Berger-Vergiat, L., Boman, E.G., 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). Portability in Trilinos [Presentation]. https://www.osti.gov/biblio/1895552 Publication ID: 76522
  • Alexander, F.J., Ang, J., Casey, T., Wolf, M., Rajamanickam, S., & Rajamanickam, S. (2021). Co-design Center for Exascale Machine Learning Technologies (ExaLearn). International Journal of High Performance Computing Applications, 35(6), pp. 598-616. https://doi.org/10.1177/10943420211029302 Publication ID: 106628
  • Adams, B.M., Bohnhoff, W.J., Dalbey, K.R., Ebeida, M.S., Eddy, J.P., Eldred, M.S., Hooper, R.W., Hough, P.D., Hu, K.T., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A.A., Seidl, D.T., Stephens, J.A., Swiler, L.P., Bachman, W.B., Winokur, J.G., & Winokur, J.G. (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
  • 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
  • Rajamanickam, S. (2021). Can Scientific Software Development Use the Outsourcing Model Successfully [Conference Paper]. https://www.osti.gov/biblio/1899526 Publication ID: 76879
  • Swiler, L.P. (2021). Verification and Validation for Cyber Emulation [Conference Presenation]. https://doi.org/10.2172/1897016 Publication ID: 76651
  • 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
  • Hughes, C., Ashraf, R., Gioiosa, R., Phillips, C.A., Berry, J., Hart, W.E., Laird, C., Rajamanickam, S., & Rajamanickam, S. (2021). ARIAA Update — SST [Presentation]. https://www.osti.gov/biblio/1897599 Publication ID: 76739
  • Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2021). Development of SNASP Machine Learned Interatomic Potentials for Materials in Extreme Environments [Conference Presenation]. https://doi.org/10.2172/1899237 Publication ID: 76870
  • Loe, J.A., Rajamanickam, S., & Rajamanickam, S. (2021). Mixed Precision in Trilinos [Conference Presenation]. https://doi.org/10.2172/1900352 Publication ID: 76971
  • Thompson, A.P. (2021). FusMatML End-of-Year Review Summary [Presentation]. https://www.osti.gov/biblio/1897015 Publication ID: 76411
  • Rajamanickam, S. (2021). Enabling Science Simulations with Scalable Computational Frameworks for Scientific Computing [Presentation]. https://www.osti.gov/biblio/1894020 Publication ID: 76394
  • 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
  • Littlewood, D.J., Wood, M.A., Montes de Zapiain, D., Rajamanickam, S., Trask, N.A., & Trask, N.A. (2021). Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]. https://doi.org/10.2172/1828106 Publication ID: 76348
  • Thompson, A.P., Aktulga, H.M., Berger, R., Bolintineanu, D.S., Brown, W.M., Crozier, P., In ‘T Veld, P.J., Kohlmeyer, A., Moore, S.G., Nguyen, T.D., Shan, R., Stevens, M.J., Tranchida, J., Trott, C.R., Plimpton, S.J., & Plimpton, S.J. (2021). $\mathrm{LAMMPS}$ – 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
  • Moore, S.G., Thompson, A.P., & Thompson, A.P. (2021). Large-Scale Atomistic Simulations [Slides]. https://doi.org/10.2172/1820306 Publication ID: 75672
  • Mariner, P.E., Berg, T.M., Debusschere, B.J., Eckert, A., Harvey, J.A., Laforce, T., Leone, R.C., Mills, M.M., Nole, M.A., Park, H.D., Perry, F.V., Seidl, D.T., Swiler, L.P., Chang, K.W., & Chang, K.W. (2021). GDSA Framework Development and Process Model Integration FY2021. https://doi.org/10.2172/1825056 Publication ID: 76168
  • Stickland, M., Li, J.D., Swiler, L.P., Tarman, T.D., & Tarman, T.D. (2021). Foundations of Rigorous Cyber Experimentation. https://doi.org/10.2172/1854751 Publication ID: 76007
  • Swiler, L.P., Brooks, D.M., & Brooks, D.M. (2021). Joint Sensitivity Analysis (JOSA) Exercise Meeting Sept. 21, 2021 [Presentation]. https://www.osti.gov/biblio/1888135 Publication ID: 75754
  • Acer, S., Boman, E.G., 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
  • Trott, C.R., Berger-Vergiat, L., Poliakoff, D., Rajamanickam, S., Lebrun-Grandie, D., Madsen, J., al Awar, N., Gligoric, M., Shipman, G., Womeldorff, G., & Womeldorff, G. (2021). The Kokkos EcoSystem: Comprehensive Performance Portability for High Performance Computing. Computing in Science and Engineering, 23(5), pp. 10-18. https://doi.org/10.1109/mcse.2021.3098509 Publication ID: 103712
  • Swiler, L.P., Becker, D., Brooks, D.M., Govaerts, J., Koskinen, L., Plischke, E., Rohlig, K., Saveleva, E., Spiessl, S.M., 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
  • Geronimo Anderson, S.I., Teranishi, K., Dunlavy, D.M., 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
  • Cusentino, M.A., Bobbitt, N.S., Wood, M.A., Thompson, A.P., & Thompson, A.P. (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
  • Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Cruz, G.J., Arguello, B., Geraci, G., Debusschere, B.J., Hanson, S.T., Outkin, A.V., Thorpe, J., Hart, W.E., Sahakian, M., Gabert, K.G., Glatter, C., Johnson, E.S., Punla-Green, A.S., & Punla-Green, A.S. (2021). Science & Engineering of Cyber Security by Uncertainty Quantification and Rigorous Experimentation (SECURE) HANDBOOK. https://doi.org/10.2172/1820527 Publication ID: 75697
  • Olivier, S.L., Ellingwood, N.D., Berry, J., Dunlavy, D.M., & Dunlavy, D.M. (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
  • Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Cruz, G.J., Arguello, B., Geraci, G., Debusschere, B.J., Hanson, S.T., Outkin, A.V., Thorpe, J., Hart, W.E., Sahakian, M., Gabert, K.G., Glatter, C., Johnson, E.S., 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
  • Garg, R., Qin, E., Martinez, F.M., Guirado, R., Jain, A., Abadal, S., Abellan, J.L., Acacio, M.E., 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
  • Wagman, B.M., Swiler, L.P., Chowdhary, K., Hillman, B.R., & Hillman, B.R. (2021). The Fingerprints of Stratospheric Aerosol Injection in E3SM. https://doi.org/10.2172/1821542 Publication ID: 75727
  • Swiler, L.P. (2021). White paper on Verification and Validation for Cyber Emulation Models. https://doi.org/10.2172/1854720 Publication ID: 75981
  • Abdelfattah, A., Anzt, H., Ayala, A., Boman, E.G., Carson, E.C., Cayrols, S., Cojean, T., Dongarra, J.J., Falgout, R., Gates, M., G, R., Higham, N.J., Kruger, S.E., Li, S., Lindquist, N., Liu, Y., Loe, J.A., Nayak, P., Osei-Kuffuor, D., … Yang, U.M. (2021). Advances in Mixed Precision Algorithms: 2021 Edition. https://doi.org/10.2172/1814447 Publication ID: 75285
  • Swiler, L.P., Basurto, E., Brooks, D.M., Eckert, A., Leone, R.C., Mariner, P.E., Portone, T., Bachman, W.B., 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.D., Swiler, L.P., Vugrin, E., Rollins, T., Cruz, G.J., Huang, H., Sahu, A., Wlazlo, P., Goulart, A., Davis, K., & Davis, K. (2021). Comparing reproduced cyber experimentation studies across different emulation testbeds [Conference Presenation]. ACM International Conference Proceeding Series. https://doi.org/10.2172/1881645 Publication ID: 79697
  • Geronimo Anderson, S.I., Teranishi, K., Dunlavy, D.M., 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
  • Rice, A., Crawford, M., Armstrong, A., Allerman, A., Modine, N.A., & Modine, N.A. (2021). Defect Spectroscopy and Reduced Compensation of UV Illuminated MOCVD n-type GaN [Conference Presenation]. https://doi.org/10.2172/1888973 Publication ID: 79658
  • Acer, S., Boman, E.G., 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
  • Swiler, L.P., Brooks, D.M., Stein, E., Rohlig, K., Plischke, E., Becker, D., Spiessl, S.M., Koskinen, L., Govaerts, J., Svitelman, V., Elena, S., & Elena, S. (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
  • Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2021). Development of SNAP Potentials for Fusion Reactor Materials [Conference Presenation]. https://doi.org/10.2172/1882069 Publication ID: 79641
  • Wood, M.A., Thompson, A.P., Cusentino, M.A., Montes de 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
  • Trott, C.R., Lebrun-Grandie, D., Arndt, D., Ciesko, J., Dang, V., Ellingwood, N.D., Gayatri, R., Harvey, E.C., Hollman, D.S., Ibanez-Granados, D.A., Liber, N., Madsen, J., Miles, J.S., Poliakoff, D., Powell, A.J., Rajamanickam, S., Simberg, M., Sunderland, D., Turcksin, B., Wilke, J., & Wilke, J. (2021). 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
  • Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2021). Properties of GMRES with Iterative Refinement on GPUs [Conference Presenation]. https://doi.org/10.2172/1884157 Publication ID: 79139
  • Tarman, T.D., Swiler, L.P., Cruz, G.J., 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.P., Debusschere, B.J., & Debusschere, B.J. (2021). Multifidelity UQ sampling for Stochastic Simulations [Conference Presenation]. https://doi.org/10.2172/1889573 Publication ID: 79490
  • Rajamanickam, S., Berger-Vergiat, L., Dang, V., Ellingwood, N.D., Harvey, E.C., 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.G., Rajamanickam, S., & Rajamanickam, S. (2021). Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs [Conference Presenation]. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 – In conjunction with IEEE IPDPS 2021. https://doi.org/10.2172/1869548 Publication ID: 78515
  • Ellis, J.A., Fielder, L., Popoola, G.A., Modine, N.A., Stephens, J.A., Thompson, A.P., Rajamanickam, S., & Rajamanickam, S. (2021). Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks. https://doi.org/10.2172/1817970 Publication ID: 79009
  • Swiler, L.P. (2021). Sensitivity Analysis for the Latest Crystalline Reference Case [Presentation]. https://www.osti.gov/biblio/1868007 Publication ID: 78432
  • Tarman, T.D., Swiler, L.P., Cruz, G.J., 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 Paper]. https://doi.org/10.1145/3474718.3474725 Publication ID: 78420
  • Stickland, M., Li, J.D., Tarman, T.D., Swiler, L.P., & Swiler, L.P. (2021). Uncertainty quantification in cyber experimentation [Conference Paper]. https://www.osti.gov/biblio/1867999 Publication ID: 78424
  • Tranchida, J., Nikolov, S.V., Wood, M.A., Cangi, A., Desjarlais, M.P., Thompson, A.P., & Thompson, A.P. (2021). Data-Driven Magneto-Elastic Predictions with Scalable Classical Spin-Lattice Dynamics [Presentation]. https://www.osti.gov/biblio/1870099 Publication ID: 78587
  • Halappanavar, M., Acer, S., Boman, E.G., 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
  • 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
  • Kramer, S., Bolintineanu, D.S., Long, K., Hamel, C., Frankel, A., Jones, R.E., Bachman, W.B., Swiler, L.P., Johnson, K.L., & Johnson, K.L. (2021). Mechanics of Materials Utilizing Machine Learning: Examples at Sandia National Laboratories [Conference Presenation]. https://doi.org/10.2172/1867564 Publication ID: 78396
  • Tranchida, J., Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (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., 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
  • Swiler, L.P., Brooks, D.M., & Brooks, D.M. (2021). GP and PCE surrogate models for estimation of sensitivity indices [Presentation]. https://www.osti.gov/biblio/1868008 Publication ID: 78433
  • Boman, E.G., 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
  • Berger-Vergiat, L., Rajamanickam, S., Dang, V., Ellingwood, N.D., Kelley, B., Harvey, E.C., Wilke, J., Acer, S., & Acer, S. (2021). Kokkos Kernels: FY20 update [Conference Presenation]. https://doi.org/10.2172/1863703 Publication ID: 78100
  • Swiler, L.P. (2021). Statistical Methods used in the Born Qualified LDRD Project [Presentation]. https://www.osti.gov/biblio/1863692 Publication ID: 78088
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May 9, 2023