Machine learning molecular dynamics simulations of liquid methanol

液相甲醇的机器学习分子动力学模拟

As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.

甲醇是结构最简单的醇,其分子间可通过氢键相连,长期以来吸引了广泛的实验和理论研究兴趣。然而,目前对该体系的理论研究主要依赖于经验力场或具有半局域密度泛函的从头算分子动力学。最近,日益精确的机器学习力场被陆续应用于体相水的研究中。受此启发,我们在色散校正的杂化泛函revPBE0-D3的精度下,报道了一个新的液相甲醇机器学习力场。该机器学习力场的分子动力学模拟速度比从头算分子动力学快几个数量级,计算得到了具有很小统计误差的径向分布函数、自扩散系数和氢键网络特性。模拟所得的结构和动力学性质与实验数据非常吻合,表明该机器学习力场相比之前的理论方法具有更高的精度。这项工作朝着对该基准体系的第一性原理描述迈出了成功的一步,并表现出机器学习力场在液相体系研究中的普适性。