Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
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DOI：
10.1063/1.4832697
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发表时间：
2013-11-28
影响因子：
4.4
通讯作者：
Guo, Hua
中科院分区：
文献类型：
--
作者：
Li, Jun;Jiang, Bin;Guo, Hua
讨论了一种用神经网络拟合整体不变势能面和置换不变势能面的严格、通用和简单的方法。这种所谓的置换不变多项式神经网络（PIP-NN）方法通过在其输入中使用一组基于PIP的对称函数来施加置换对称性。对于多于三个原子的系统，它表明，在输入矢量中的对称函数的数量需要大于内部坐标的数量，以包括初级和次级不变多项式。这种PIP-NN方法在三原子-三原子反应系统中得到了成功的验证，得到了平均误差为meV的全维全局PES。这些PES用于全维量子动力学计算。(C)2013 AIP Publishing LLC.
A rigorous, general, and simple method to fit global and permutation invariant potential energy surfaces (PESs) using neural networks (NNs) is discussed. This so-called permutation invariant polynomial neural network (PIP-NN) method imposes permutation symmetry by using in its input a set of symmetry functions based on PIPs. For systems with more than three atoms, it is shown that the number of symmetry functions in the input vector needs to be larger than the number of internal coordinates in order to include both the primary and secondary invariant polynomials. This PIP-NN method is successfully demonstrated in three atom-triatomic reactive systems, resulting in full-dimensional global PESs with average errors on the order of meV. These PESs are used in full-dimensional quantum dynamical calculations. (C) 2013 AIP Publishing LLC.