论文标题
通过从路径积分分子动力学得出的神经网络学习的质心可以极大地加速质心分子动力学
Centroid Molecular Dynamics Can Be Greatly Accelerated Through Neural Network Learned Centroid Forces Derived from Path Integral Molecular Dynamics
论文作者
论文摘要
在过去的30年中，质心分子动力学（CMD）已被证明是一种可行的经典相位空间公式，用于计算量子动力学特性。但是，对质心有效力的计算仍然是一个重要的计算成本，并限制了CMD成为研究凝结相位量子动力学的有效方法。在本文中，我们引入了一种基于神经网络的方法，用于首先从路径积分分子动力学数据中学习质心有效力，该数据随后用作直接使用CMD算法直接进化质心的有效力场。这种称为机器学习的质心分子动力学（ML-CMD）的方法比Fly CMD和环聚合物分子动力学（RPMD）更快且昂贵的速度要快得多。 ML-CMD的培训方面也可以使用DEEPMD软件套件直接实现。然后将ML-CMD应用于两个模型系统，以说明方法：液体para-Hydra和水。在量子动力学特性的估计中，结果表明与CMD和RPMD的精度相当，包括自扩散常数和速度时间相关函数，但显着降低了总体计算成本。
For nearly the past 30 years, Centroid Molecular Dynamics (CMD) has proven to be a viable classical-like phase space formulation for the calculation of quantum dynamical properties. However, calculation of the centroid effective force remains a significant computational cost and limits the ability of CMD to be an efficient approach to study condensed phase quantum dynamics. In this paper we introduce a neural network-based methodology for first learning the centroid effective force from path integral molecular dynamics data, which is subsequently used as an effective force field to evolve the centroids directly with the CMD algorithm. This method, called Machine-Learned Centroid Molecular Dynamics (ML-CMD) is faster and far less costly than both standard on the fly CMD and ring polymer molecular dynamics (RPMD). The training aspect of ML-CMD is also straightforwardly implemented utilizing the DeepMD software kit. ML-CMD is then applied to two model systems to illustrate the approach: liquid para-hydrogen and water. The results show comparable accuracy to both CMD and RPMD in the estimation of quantum dynamical properties, including the self-diffusion constant and velocity time correlation function, but for significantly reduced overall computational cost.
