Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA.
J Chem Phys. 2013 Aug 7;139(5):054112. doi: 10.1063/1.4817187.
A simple, general, and rigorous scheme for adapting permutation symmetry in molecular systems is proposed and tested for fitting global potential energy surfaces using neural networks (NNs). The symmetry adaptation is realized by using low-order permutation invariant polynomials (PIPs) as inputs for the NNs. This so-called PIP-NN approach is applied to the H + H2 and Cl + H2 systems and the analytical potential energy surfaces for these two systems were accurately reproduced by PIP-NN. The accuracy of the NN potential energy surfaces was confirmed by quantum scattering calculations.
提出并测试了一种简单、通用且严格的分子体系中置换对称性适应方案，该方案使用神经网络（NN）拟合全局势能面。通过使用低阶置换不变多项式（PIP）作为 NN 的输入来实现对称性适应。该所谓的 PIP-NN 方法应用于 H + H2 和 Cl + H2 体系，并且通过 PIP-NN 准确地再现了这两个体系的分析势能面。NN 势能面的准确性通过量子散射计算得到了确认。
