Zhou Xueyao, Nattino Francesco, Zhang Yaolong, Chen Jun, Kroes Geert-Jan, Guo Hua, Jiang Bin
Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Phys Chem Chem Phys. 2017 Nov 22;19(45):30540-30550. doi: 10.1039/c7cp05993k.
A fifteen-dimensional global potential energy surface for the dissociative chemisorption of methane on the rigid Ni(111) surface is developed by a high fidelity fit of ∼200 000 DFT energy points computed using a specific reaction parameter density functional designed to reproduce experimental data. The permutation symmetry and surface periodicity are rigorously enforced using the permutation invariant polynomial-neural network approach. The fitting accuracy of the potential energy surface is thoroughly investigated by examining both static and dynamical attributes of CHD dissociation on the frozen surface. This potential energy surface is expected to be chemically accurate as after correction for surface temperature effects it reproduces the measured initial sticking probabilities of CHD on Ni(111) for various incidence conditions.
通过使用旨在重现实验数据的特定反应参数密度泛函计算得到的约200000个密度泛函理论（DFT）能量点进行高保真拟合，构建了甲烷在刚性Ni(111)表面解离化学吸附的十五维全局势能面。使用置换不变多项式神经网络方法严格执行置换对称性和表面周期性。通过研究CHD在冻结表面解离的静态和动态属性，全面研究了势能面的拟合精度。经表面温度效应校正后，该势能面能够重现不同入射条件下CHD在Ni(111)上测量的初始 sticking概率，预计具有化学准确性。
