Li Jun, Guo Hua
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China.
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA.
J Chem Phys. 2015 Dec 7;143(21):214304. doi: 10.1063/1.4936660.
The permutation invariant polynomial-neural network (PIP-NN) approach is extended to fit intermolecular potential energy surfaces (PESs). Specifically, three PESs were constructed for the Ne-C2H2 system. PES1 is a full nine-dimensional PIP-NN PES directly fitted to ∼42 000 ab initio points calculated at the level of CCSD(T)-F12a/cc-pCVTZ-F12, while the other two consist of the six-dimensional PES for C2H2 [H. Han, A. Li, and H. Guo, J. Chem. Phys. 141, 244312 (2014)] and an intermolecular PES represented in either the PIP (PES2) or PIP-NN (PES3) form. The comparison of fitting errors and their distributions, one-dimensional cuts and two-dimensional contour plots of the PESs, as well as classical trajectory collisional energy transfer dynamics calculations shows that the three PESs are very similar. We conclude that full-dimensional PESs for non-covalent interacting molecular systems can be constructed efficiently and accurately by the PIP-NN approach for both the constituent molecules and intermolecular parts.
排列不变多项式神经网络(PIP-NN)方法被扩展用于拟合分子间势能面(PESs)。具体而言,为Ne-C2H2体系构建了三个PESs。PES1是一个完整的九维PIP-NN PES,直接拟合到在CCSD(T)-F12a/cc-pCVTZ-F12水平计算得到的约42000个从头算点,而另外两个由C2H2的六维PES [H. Han, A. Li, and H. Guo, J. Chem. Phys. 141, 244312 (2014)] 以及以PIP(PES2)或PIP-NN(PES3)形式表示的分子间PES组成。对PESs的拟合误差及其分布、一维截面和二维等高线图的比较,以及经典轨迹碰撞能量转移动力学计算表明,这三个PESs非常相似。我们得出结论,通过PIP-NN方法可以高效且准确地为非共价相互作用分子体系的组成分子和分子间部分构建全维PESs。