OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.
J Phys Chem B
; 128(1): 109-116, 2024 01 11.
Article
de En
| MEDLINE
| ID: mdl-38154096
ABSTRACT
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Eau
/
Simulation de dynamique moléculaire
Langue:
En
Journal:
J Phys Chem B
/
J. phys. chem. B
/
The journal of physical chemistry. B (1997 : Online)
Sujet du journal:
QUIMICA
Année:
2024
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique
Pays de publication:
États-Unis d'Amérique