Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain
Extending the Time Scales of Nonadiabatic Molecular Dynamics via Machine Learning in the Time Domain
复制标题
用机器学习扩展非绝热分子动力学的时间尺度
DOI：
10.1021/acs.jpclett.1c03823
复制
发表时间：
2021-12-16
影响因子：
5.7
通讯作者：
Akimov, Alexey, V
中科院分区：
文献类型：
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作者：
Akimov, Alexey, V
开发了一种在扩展纳米级和固态系统中直接建模长期尺度非绝热动力学的新方法。所提出的方法能够通过在时域中直接训练的机器学习模型将电子振动哈密顿量预测为时间的直接函数。事实证明，使用周期性和非周期性函数将时间转换为人工神经网络的有效输入模式对于这种适用于抽象模型和原子模型的方法至关重要。探讨和讨论了与新方法相关的最佳策略和可能的局限性。对含双空位的单层黑磷系统进行了前所未有的长 20 皮秒轨迹的示例性直接模拟，并证明了进行此类扩展模拟的重要性。提出了对该系统中激发态光物理学的新见解，包括退相干的作用和模型定义。
A novel methodology for direct modeling of long-time scale nonadiabatic dynamics in extended nanoscale and solid-state systems is developed. The presented approach enables forecasting the vibronic Hamiltonians as a direct function of time via machine-learning models trained directly in the time domain. The use of periodic and aperiodic functions that transform time into effective input modes of the artificial neural network is demonstrated to be essential for such an approach to work for both abstract and atomistic models. The best strategies and possible limitations pertaining to the new methodology are explored and discussed. An exemplary direct simulation of unprecedentedly long 20 picosecond trajectories is conducted for a divacancy-containing monolayer black phosphorus system, and the importance of conducting such extended simulations is demonstrated. New insights into the excited states photophysics in this system are presented, including the role of decoherence and model definition.