Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
S. H. Balantrapu, A. C. Thakur, C. Benmore, and G. Sivaraman.
Publications & Patents
Selected publications and preprints spanning machine-learned interatomic potentials, experiment-guided atomistic modeling, molecular machine learning, and high-performance AI workflows.
Preprints
LifeLong Learning for Large Language Models in Predicting Chemical Reaction Yields
G. Sivaraman, N. Jackson, and K. Raghavan.
Selected Published Works
Molecular Simulation of Functionalized Covalent Organic Framework Membranes for Inorganic Salt Separation
G. Wei, A. Narayanan Krishnamoorthy, G. Sivaraman, and L. Valentino.
Unraveling the Structure of Molten Salts: A Review of High-Energy X-ray Diffraction (HEXRD) Studies
A. L. Blockmon, D. Johnson, J. Guo, G. Sivaraman, N. C. Hoyt, and C. J. Benmore.
Reciprocal Space Attention for Learning Long-Range Interactions
H. Ramasubramanian, A. Vazquez-Mayagoitia, G. Sivaraman, and A. C. Thakur.
Evidence of Short Chains in Liquid Sulfur
C. J. Benmore and G. Sivaraman.
Deciphering Diffuse Scattering with Machine Learning and the Equivariant Foundation Model: The Case of Molten FeO
G. Sivaraman and C. J. Benmore.
Plutonium Oxide Melt Structure and Covalency
S. K. Wilke, C. J. Benmore, O. L. G. Alderman, G. Sivaraman, M. D. Ruehl, and coauthors.
Applying Machine Learning and Quantum Chemistry to Predict the Glass Transition Temperatures of Polymers
K. Hickey, J. Feinstein, G. Sivaraman, M. MacDonell, E. Yan, C. Matherson, and coauthors.
AL4GAP: Active Learning Workflow for Generating DFT-SCAN Accurate Machine-Learning Potentials for Combinatorial Molten Salt Mixtures
J. Guo, V. Woo, D. A. Andersson, N. Hoyt, M. Williamson, I. Foster, C. Benmore, G. Sivaraman, and coauthors.
Machine Learning Interatomic Potential for Silicon-Nitride (Si3N4) by Active Learning
D. Milardovich, C. Wilhelmer, D. Waldhoer, L. Cvitkovich, G. Sivaraman, and coauthors.
Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
L. Ward, J. G. Pauloski, V. Hayot-Sasson, R. Chard, Y. Babuji, G. Sivaraman, and coauthors.
A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides
G. Sivaraman, G. Csanyi, A. Vazquez-Mayagoitia, I. T. Foster, S. K. Wilke, and coauthors.
Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by High-Energy X-ray Diffraction
J. Guo, L. Ward, Y. Babuji, N. Hoyt, M. Williamson, I. Foster, N. Jackson, G. Sivaraman, and coauthors.
Structural Phase Transitions between Layered Indium Selenide for Integrated Photonic Memory
T. Li, Y. Wang, W. Li, D. Mao, C. J. Benmore, I. Evangelista, H. Xing, Q. Li, G. Sivaraman, and coauthors.
Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning
G. Sivaraman and N. E. Jackson.
Uncertainty-Informed Deep Transfer Learning of Perfluoroalkyl and Polyfluoroalkyl Substance Toxicity
J. Feinstein, G. Sivaraman, K. Picel, B. Peters, A. Vazquez-Mayagoitia, and coauthors.
Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing
L. Ward, G. Sivaraman, J. G. Pauloski, Y. Babuji, R. Chard, N. Dandu, and coauthors.
Co-Design Center for Exascale Machine Learning Technologies (ExaLearn)
F. J. Alexander, J. Ang, J. A. Bilbrey, J. Balewski, T. Casey, R. Chard, J. Choi, G. Sivaraman, and coauthors.
Proxima: Accelerating the Integration of Machine Learning in Atomistic Simulations
Y. Zamora, L. Ward, G. Sivaraman, I. Foster, and H. Hoffmann.
Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl
G. Sivaraman, J. Guo, L. Ward, N. Hoyt, M. Williamson, I. Foster, C. Benmore, and coauthors.
Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
G. Sivaraman, L. Gallington, A. N. Krishnamoorthy, M. Stan, G. Csanyi, and coauthors.
DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning
S. Tovey, A. Narayanan Krishnamoorthy, G. Sivaraman, J. Guo, C. Benmore, and coauthors.
Machine-Learned Interatomic Potentials by Active Learning: Amorphous and Liquid Hafnium Dioxide
G. Sivaraman, A. N. Krishnamoorthy, M. Baur, C. Holm, M. Stan, G. Csanyi, and coauthors.
A Machine Learning Workflow for Molecular Analysis: Application to Melting Points
G. Sivaraman, N. E. Jackson, B. Sanchez-Lengeling, A. Vazquez-Mayagoitia, and coauthors.
Electrically Sensing Hachimoji DNA Nucleotides through a Hybrid Graphene/h-BN Nanopore
F. A. L. de Souza, G. Sivaraman, M. Fyta, R. H. Scheicher, W. L. Scopel, and coauthors.