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 on simulations of cyclin-dependent
kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water.
Taken together, these features make it practical to use machine learning to
improve the accuracy of simulations at only a modest increase in cost.

机器学习在分子模拟中扮演重要且日益增长的角色，OpenMM 分子动力学工具包的最新版本引入了新特性，支持使用机器学习模型进行模拟优化。