Accelerating molecular dynamic simulation on graphics processing units. - Mark S. Friedrichs; Peter K. Eastman; Vaidyanathan Vishal; Mike Houston; Scott LeGrand; Adam L. Beberg; Daniel L. Ensign; Christopher M. Bruns; Vijay S. Pande (J. Comput. Chem., 2009)

OpenMM: A Hardware-Independent Framework for Molecular Simulations. - Peter K. Eastman; Vijay S. Pande (Comput. Sci. Eng., 2010)

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. - Peter K. Eastman; Vijay S. Pande (J. Comput. Chem., 2010)

Fast Flexible Modeling of RNA Structure Using Internal Coordinates. - Samuel Coulbourn Flores; Michael A. Sherman; Christopher M. Bruns; Peter K. Eastman; Russ B. Altman (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. - Baofeng Zhang; Denise Kilburg; Peter K. Eastman; Vijay S. Pande; Emilio Gallicchio (J. Comput. Chem., 2017)

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. - Peter K. Eastman; Jason M. Swails; John D. Chodera; Robert McGibbon; Yutong Zhao; Kyle Beauchamp; Lee-Ping Wang; Andrew C. Simmonett; Matthew P. Harrigan; Chaya D. Stern; Rafal P. Wiewiora; Bernard R. Brooks; et al. (PLoS Comput. Biol., 2017)

Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss. - Rishi Sharma 005; Amir Barati Farimani; Joe Gomes; Peter K. Eastman; Vijay S. Pande (CoRR, 2018)

Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks. - Jing Huang 004; Justin A. Lemkul; Peter K. Eastman; Alexander D. MacKerell Jr. (J. Comput. Chem., 2018)

Solving the RNA design problem with reinforcement learning. - Peter K. Eastman; Jade Shi; Bharath Ramsundar; Vijay S. Pande (PLoS Comput. Biol., 2018)

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (CoRR, 2022)

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. - Peter K. Eastman; Pavan Kumar Behara; David L. Dotson; Raimondas Galvelis; John E. Herr; Josh T. Horton; Yuezhi Mao; John D. Chodera; Benjamin P. Pritchard; Yuanqing Wang; Gianni De Fabritiis; Thomas E. Markland (CoRR, 2022)

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. - Peter K. Eastman; Raimondas Galvelis; Raúl P. Peláez; Charlles R. A. Abreu; Stephen E. Farr; Emilio Gallicchio; Anton Gorenko; Michael M. Henry; Frank Hu; Jing Huang 004; Andreas Krämer; Julien Michel; et al. (CoRR, 2023)

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. - Raimondas Galvelis; Alejandro Varela-Rial; Stefan Doerr; Roberto Fino; Peter K. Eastman; Thomas E. Markland; John D. Chodera; Gianni De Fabritiis (J. Chem. Inf. Model., 2023)

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. - Raúl P. Peláez; Guillem Simeon; Raimondas Galvelis; Antonio Mirarchi; Peter K. Eastman; Stefan Doerr; Philipp Thölke; Thomas E. Markland; Gianni De Fabritiis (CoRR, 2024)

Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. - Peter K. Eastman; Benjamin P. Pritchard; John D. Chodera; Thomas E. Markland (CoRR, 2024)

On the design space between molecular mechanics and machine learning force fields. - Yuanqing Wang; Kenichiro Takaba; Michael S. Chen; Marcus Wieder; Yuzhi Xu; Tong Zhu; John Z. H. Zhang; Arnav Nagle; Kuang Yu; Xinyan Wang; Daniel J. Cole; Joshua A. Rackers; et al. (CoRR, 2024)