Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

中文翻译：

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借助机器学习进行内力校正，以进行量子力学/分子力学模拟。

从头算起的量子力学/分子力学（QM / MM）分子动力学模拟是一种有用的工具，可以计算热力学性质，例如化学反应的平均力势，但非常耗时。在本文中，我们开发了一种使用内力校正的新方法，该方法用于具有预定义反应坐标的低水平半经验QM / MM分子动力学采样。作为一个修正项，内力是通过机器学习方案预测的，该方案提供了一个复杂的力场，并在每个积分步骤中将原子力添加到了反应坐标相关原子上。我们将此方法应用于水溶液中的两个反应，并在从头算起的QM / MM水平上重现了平均力的潜力。计算成本的节省约为2个数量级。