Raff L M, Malshe M, Hagan M, Doughan D I, Rockley M G, Komanduri R
Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA.
J Chem Phys. 2005 Feb 22;122(8):84104. doi: 10.1063/1.1850458.
A neural network/trajectory approach is presented for the development of accurate potential-energy hypersurfaces that can be utilized to conduct ab initio molecular dynamics (AIMD) and Monte Carlo studies of gas-phase chemical reactions, nanometric cutting, and nanotribology, and of a variety of mechanical properties of importance in potential microelectromechanical systems applications. The method is sufficiently robust that it can be applied to a wide range of polyatomic systems. The overall method integrates ab initio electronic structure calculations with importance sampling techniques that permit the critical regions of configuration space to be determined. The computed ab initio energies and gradients are then accurately interpolated using neural networks (NN) rather than arbitrary parametrized analytical functional forms, moving interpolation or least-squares methods. The sampling method involves a tight integration of molecular dynamics calculations with neural networks that employ early stopping and regularization procedures to improve network performance and test for convergence. The procedure can be initiated using an empirical potential surface or direct dynamics. The accuracy and interpolation power of the method has been tested for two cases, the global potential surface for vinyl bromide undergoing unimolecular decomposition via four different reaction channels and nanometric cutting of silicon. The results show that the sampling methods permit the important regions of configuration space to be easily and rapidly identified, that convergence of the NN fit to the ab initio electronic structure database can be easily monitored, and that the interpolation accuracy of the NN fits is excellent, even for systems involving five atoms or more. The method permits a substantial computational speed and accuracy advantage over existing methods, is robust, and relatively easy to implement.
本文提出了一种神经网络/轨迹方法，用于开发精确的势能超曲面，该超曲面可用于进行气相化学反应、纳米切割和纳米摩擦学以及潜在微机电系统应用中各种重要机械性能的从头算分子动力学（AIMD）和蒙特卡罗研究。该方法足够稳健，可应用于广泛的多原子系统。整体方法将从头算电子结构计算与重要性采样技术相结合，从而能够确定构型空间的关键区域。然后，使用神经网络（NN）而非任意参数化的解析函数形式、移动插值或最小二乘法，对计算得到的从头算能量和梯度进行精确插值。采样方法涉及分子动力学计算与神经网络的紧密结合，神经网络采用提前停止和正则化程序来提高网络性能并测试收敛性。该程序可以使用经验势能面或直接动力学来启动。该方法的准确性和插值能力已针对两种情况进行了测试，即溴乙烯通过四种不同反应通道进行单分子分解的全局势能面以及硅的纳米切割。结果表明，采样方法能够轻松快速地识别构型空间的重要区域，能够轻松监测神经网络对从头算电子结构数据库的拟合收敛情况，并且即使对于涉及五个或更多原子的系统，神经网络拟合的插值精度也非常出色。该方法相对于现有方法具有显著的计算速度和准确性优势，稳健且相对易于实现。
