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Peter K. Eastman
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2020 – today
- 2024
- [i6]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 abs/2402.17660 (2024) - [i5]Peter K. Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland:
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. CoRR abs/2406.13112 (2024) - 2023
- [j9]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): 5701-5708 (2023) - [i4]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 abs/2310.03121 (2023) - 2022
- [i3]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 abs/2201.08110 (2022) - [i2]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 abs/2209.10702 (2022)
2010 – 2019
- 2018
- [j8]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): 1682-1689 (2018) - [j7]Peter K. Eastman, Jade Shi, Bharath Ramsundar, Vijay S. Pande:
Solving the RNA design problem with reinforcement learning. PLoS Comput. Biol. 14(6) (2018) - [i1]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 abs/1807.11374 (2018) - 2017
- [j6]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): 740-752 (2017) - [j5]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) - 2011
- [j4]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): 1247-1257 (2011) - 2010
- [j3]Peter K. Eastman, Vijay S. Pande:
OpenMM: A Hardware-Independent Framework for Molecular Simulations. Comput. Sci. Eng. 12(4): 34-39 (2010) - [j2]Peter K. Eastman, Vijay S. Pande:
Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31(6): 1268-1272 (2010)
2000 – 2009
- 2009
- [j1]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): 864-872 (2009)
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