Peter K. Eastman

According to our database, Peter K. Eastman authored at least 14 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

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

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., September, 2023

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, 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

CoRR, 2023

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

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

2018

Solving the RNA design problem with reinforcement learning.

Peter K. Eastman, Jade Shi, Bharath Ramsundar, Vijay S. Pande

PLoS Comput. Biol., 2018

Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: Implementation, validation, and benchmarks.

Jing Huang, Justin A. Lemkul, Peter K. Eastman, Alexander D. MacKerell Jr.

J. Comput. Chem., 2018

Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss.

Rishi Sharma, Amir Barati Farimani, Joe Gomes, Peter K. Eastman, Vijay S. Pande

CoRR, 2018

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, Vijay S. Pande

PLoS Comput. Biol., 2017

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

2011

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

2010

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit.

Peter K. Eastman, Vijay S. Pande

J. Comput. Chem., 2010

OpenMM: A Hardware-Independent Framework for Molecular Simulations.

Peter K. Eastman, Vijay S. Pande

Comput. Sci. Eng., 2010

2009

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