论文标题
与长期短期内存网络的最少切口跳跃跳跃
Fewest-Switches Surface Hopping with Long Short-Term Memory Networks
论文作者
论文摘要
混合量子古典动力学模拟对于研究光体物理和光化学中的非绝热现象至关重要。近年来，已经开发出许多机器学习模型来加速核子系统的时间演变。本文中，我们实施了长期的短期内存（LSTM）网络，作为传播器，以加速最少的开关表面跳跃（FSSH）模拟期间电子子系统的时间演变。使用原始FSSH方法生成了少量的参考轨迹，然后可以构建LSTM网络，并仔细检查典型的LSTM-FSSH轨迹，这些典型LSTM-FSSH轨迹采用与相应参考相同的初始条件和随机数。将构造的网络应用于FSSH，以进一步生成轨迹集合，以揭示非绝热过程的机制。以塔利的三个模型为测试系统，可以定性地再现集体结果。这项工作表明LSTM适用于最受欢迎的表面跳跃模拟。
The mixed quantum-classical dynamical simulation is essential to study nonadiabatic phenomena in photophysics and photochemistry. In recent years, many machine learning models have been developed to accelerate the time evolution of the nuclear subsystem. Herein, we implement long short-term memory (LSTM) networks as a propagator to accelerate the time evolution of the electronic subsystem during the fewest-switches surface hopping (FSSH) simulations. A small number of reference trajectories are generated using the original FSSH method, and then the LSTM networks can be built, accompanied by careful examination of typical LSTM-FSSH trajectories that employ the same initial condition and random numbers as the corresponding reference. The constructed network is applied to FSSH to further produce a trajectory ensemble to reveal the mechanism of nonadiabatic processes. Taking Tully's three models as test systems, the collective results can be reproduced qualitatively. This work demonstrates that LSTM is applicable to the most popular surface hopping simulations.
