Kolb Brian, Zhao Bin, Li Jun, Jiang Bin, Guo Hua
Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331, China.
J Chem Phys. 2016 Jun 14;144(22):224103. doi: 10.1063/1.4953560.
The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H2 → H2 + H, H + H2O → H2 + OH, and H + CH4 → H2 + CH3. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.
在H + H2 → H2 + H、H + H2O → H2 + OH和H + CH4 → H2 + CH3这三个体系中，对用于拟合反应势能面的贝赫勒-帕里内洛原子神经网络方法的适用性和准确性进行了严格检验。提出了一种实用的蒙特卡罗方法，以有效地选择以原子为中心的映射函数。势能面的准确性不仅通过拟合误差进行检验，还通过在动态重要区域的直接比较以及量子散射计算进行验证。我们的结果表明，即使涉及离解连续区，该方法在表示多维势能面时也既准确又高效。
