通讯：用基本不变神经网络拟合势能面

Communication: Fitting potential energy surfaces with fundamental invariant neural network.

作者信息

Shao Kejie, Chen Jun, Zhao Zhiqiang, Zhang Dong H

机构信息

State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People's Republic of China and University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.

出版信息

J Chem Phys. 2016 Aug 21;145(7):071101. doi: 10.1063/1.4961454.

DOI:10.1063/1.4961454

PMID:27544080

Abstract

A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH3 and CH4 were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

摘要

在构建涉及相同原子的分子系统的势能面时，提出了一种更灵活的神经网络（NN）方法，该方法使用基本不变量（FIs）作为输入向量。在数学上，FIs有限生成置换不变多项式（PIP）环。与NN相结合，基本不变量神经网络（FI-NN）可以将任何函数逼近到任意精度。由于FI-NN最小化了输入置换不变多项式的大小，它可以有效地减少势能的评估时间，特别是对于多原子系统。在这项工作中，我们提供了所有可能的至多五个原子的分子系统的FIs。使用FI-NN构建了OH3和CH4的势能面，通过全维量子动态散射和束缚态计算证实了其准确性。