OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.
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OpenMM 8:具有机器学习潜力的分子动力学模拟。

DOI:
10.1021/acs.jpcb.3c06662
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发表时间:
2024
期刊:
The journal of physical chemistry. B
影响因子:
--
通讯作者:
Eastman P
Eastman P
中科院分区:
--
文献类型:
--
作者:
Eastman P

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机器学习在分子模拟中发挥着重要且不断增长的作用。最新版本的OpenMM分子动力学工具包引入了新功能,以支持机器学习潜力的使用。任意PyTorch模型都可以添加到仿真中,并用于计算力和能量。更高级别的界面允许用户使用通用的、预先训练的潜在函数轻松地对他们感兴趣的分子进行建模。优化的CUDA内核和自定义PyTorch操作的集合大大提高了模拟的速度。我们在模拟细胞周期蛋白依赖性激酶8(CDK8)和水中的绿色荧光蛋白发色团中展示了这些功能。总的来说,这些功能使得使用机器学习来提高模拟的准确性变得切实可行,而成本只有适度的增加。
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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