Peter K. Eastman

16 papers Journal 16
YearRankTypeTitle / Venue / Authors
2024 J jnl
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning.
CoRR
Peter K. Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
2024 J jnl
On the design space between molecular mechanics and machine learning force fields.
CoRR
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, Kyunghyun Cho, Joe G. Greener, Peter K. Eastman, Stefano Martiniani, Mark E. Tuckerman
2024 J jnl
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.
CoRR
Raúl P. Peláez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter K. Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis
2023 J jnl
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.
J. Chem. Inf. Model.
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
2023 J jnl
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.
CoRR
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, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João P. G. L. M. Rodrigues, Jaime Rodríguez-Guerra, Andrew C. Simmonett, Jason M. Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland
2022 J jnl
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics.
CoRR
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
2022 J jnl
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.
CoRR
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
2018 J jnl
Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks.
J. Comput. Chem.
Jing Huang, Justin A. Lemkul, Peter K. Eastman, Alexander D. MacKerell Jr.
2018 J jnl
Solving the RNA design problem with reinforcement learning.
PLoS Comput. Biol.
Peter K. Eastman, Jade Shi, Bharath Ramsundar, Vijay S. Pande
2018 J jnl
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss.
CoRR
Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter K. Eastman, Vijay S. Pande
2017 J jnl
Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units.
J. Comput. Chem.
Baofeng Zhang, Denise Kilburg, Peter K. Eastman, Vijay S. Pande, Emilio Gallicchio
2017 J jnl
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics.
PLoS Comput. Biol.
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, Vijay S. Pande
2011 J jnl
Fast Flexible Modeling of RNA Structure Using Internal Coordinates.
IEEE ACM Trans. Comput. Biol. Bioinform.
Samuel Coulbourn Flores, Michael A. Sherman, Christopher M. Bruns, Peter K. Eastman, Russ B. Altman
2010 J jnl
Efficient nonbonded interactions for molecular dynamics on a graphics processing unit.
J. Comput. Chem.
Peter K. Eastman, Vijay S. Pande
2010 J jnl
OpenMM: A Hardware-Independent Framework for Molecular Simulations.
Comput. Sci. Eng.
Peter K. Eastman, Vijay S. Pande
2009 J jnl
Accelerating molecular dynamic simulation on graphics processing units.
J. Comput. Chem.
Mark S. Friedrichs, Peter K. Eastman, Vaidyanathan Vishal, Mike Houston, Scott LeGrand, Adam L. Beberg, Daniel L. Ensign, Christopher M. Bruns, Vijay S. Pande