Peter K. Eastman

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 11

Top Topics

Molecular Dynamics

Top Venues

J. Comput. Chem.

PLoS Comput. Biol.

J. Chem. Inf. Model.

Publications

Peter K. Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. CoRR (2024)
Raúl P. Peláez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter K. Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De Fabritiis
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. CoRR (2024)
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
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. CoRR (2023)
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. J. Chem. Inf. Model. 63 (18) (2023)
Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter K. Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics. CoRR (2022)
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
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. CoRR (2022)
Jing Huang, Justin A. Lemkul, Peter K. Eastman, Alexander D. MacKerell Jr.
Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks. J. Comput. Chem. 39 (21) (2018)
Peter K. Eastman, Jade Shi, Bharath Ramsundar, Vijay S. Pande
Solving the RNA design problem with reinforcement learning. PLoS Comput. Biol. 14 (6) (2018)
Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter K. Eastman, Vijay S. Pande
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss. CoRR (2018)
Baofeng Zhang, Denise Kilburg, Peter K. Eastman, Vijay S. Pande, Emilio Gallicchio
Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. J. Comput. Chem. 38 (10) (2017)
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
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput. Biol. 13 (7) (2017)
Samuel Coulbourn Flores, Michael A. Sherman, Christopher M. Bruns, Peter K. Eastman, Russ B. Altman
Fast Flexible Modeling of RNA Structure Using Internal Coordinates. IEEE ACM Trans. Comput. Biol. Bioinform. 8 (5) (2011)
Peter K. Eastman, Vijay S. Pande
OpenMM: A Hardware-Independent Framework for Molecular Simulations. Comput. Sci. Eng. 12 (4) (2010)
Peter K. Eastman, Vijay S. Pande
Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31 (6) (2010)
Mark S. Friedrichs, Peter K. Eastman, Vaidyanathan Vishal, Mike Houston, Scott LeGrand, Adam L. Beberg, Daniel L. Ensign, Christopher M. Bruns, Vijay S. Pande
Accelerating molecular dynamic simulation on graphics processing units. J. Comput. Chem. 30 (6) (2009)