Department of Chemical Engineering, University of Southern California, Los Angeles, California 90089, United States.
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
J Phys Chem Lett. 2021 Jul 8;12(26):6070-6077. doi: 10.1021/acs.jpclett.1c01645. Epub 2021 Jun 25.
Nonadiabatic (NA) molecular dynamics (MD) allows one to study far-from-equilibrium processes involving excited electronic states coupled to atomic motions. While NAMD involves expensive calculations of excitation energies and NA couplings (NACs), ground-state properties require much less effort and can be obtained with machine learning (ML) at a fraction of the ab initio cost. Application of ML to excited states and NACs is more challenging, due to costly reference methods, many states, and complex geometry dependence. We developed a NAMD methodology that avoids time extrapolation of excitation energies and NACs. Instead, under the classical path approximation that employs a precomputed ground-state trajectory, we use a small fraction (2%) of the geometries to train neural networks and obtain excited-state energies and NACs for the remaining 98% of the geometries by interpolation. Demonstrated with metal halide perovskites that exhibit complex MD, the method provides nearly two orders of computational savings while generating accurate NAMD results.
非绝热（NA）分子动力学（MD）允许研究涉及与原子运动耦合的激发电子态的远非平衡过程。虽然 NAMD 需要计算激发能和非绝热耦合（NAC）的昂贵计算，但基态性质需要的工作量要少得多，可以通过机器学习（ML）以低于从头算成本的分数获得。由于昂贵的参考方法、许多状态和复杂的几何依赖性，将 ML 应用于激发态和 NAC 更具挑战性。我们开发了一种避免激发能和 NAC 时间外推的 NAMD 方法。相反，在采用预先计算的基态轨迹的经典路径近似下，我们使用一小部分（2%）的结构来训练神经网络，并通过插值获得其余 98%的结构的激发态能量和 NAC。该方法在展示具有复杂 MD 的金属卤化物钙钛矿时，提供了近两个数量级的计算节省，同时生成了准确的 NAMD 结果。
