OpenMM 8 : Molecular Dynamics Simulation with Machine Learning Potentials
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:128 |
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| Enthalten in: |
The journal of physical chemistry. B - 128(2024), 1 vom: 11. Jan., Seite 109-116 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Eastman, Peter [VerfasserIn] |
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| Anmerkungen: |
Date Completed 12.01.2024 Date Revised 10.02.2024 published: Print-Electronic UpdateOf: ArXiv. 2023 Nov 29;:. - PMID 37986730 Citation Status MEDLINE |
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| doi: |
10.1021/acs.jpcb.3c06662 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36644736X |
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| 520 | |a Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost | ||
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| 700 | 1 | |a Peláez, Raúl P |e verfasserin |4 aut | |
| 700 | 1 | |a Abreu, Charlles R A |e verfasserin |4 aut | |
| 700 | 1 | |a Farr, Stephen E |e verfasserin |4 aut | |
| 700 | 1 | |a Gallicchio, Emilio |e verfasserin |4 aut | |
| 700 | 1 | |a Gorenko, Anton |e verfasserin |4 aut | |
| 700 | 1 | |a Henry, Michael M |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Frank |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Krämer, Andreas |e verfasserin |4 aut | |
| 700 | 1 | |a Michel, Julien |e verfasserin |4 aut | |
| 700 | 1 | |a Mitchell, Joshua A |e verfasserin |4 aut | |
| 700 | 1 | |a Pande, Vijay S |e verfasserin |4 aut | |
| 700 | 1 | |a Rodrigues, João Pglm |e verfasserin |4 aut | |
| 700 | 1 | |a Rodriguez-Guerra, Jaime |e verfasserin |4 aut | |
| 700 | 1 | |a Simmonett, Andrew C |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Sukrit |e verfasserin |4 aut | |
| 700 | 1 | |a Swails, Jason |e verfasserin |4 aut | |
| 700 | 1 | |a Turner, Philip |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanqing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ivy |e verfasserin |4 aut | |
| 700 | 1 | |a Chodera, John D |e verfasserin |4 aut | |
| 700 | 1 | |a De Fabritiis, Gianni |e verfasserin |4 aut | |
| 700 | 1 | |a Markland, Thomas E |e verfasserin |4 aut | |
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