In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties for photodynamics simulations. The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings. The nonadiabatic couplings are learned in a phase-free manner as derivatives of a virtually constructed property by the deep learning model, which guarantees rotational covariance. Additionally, an approximation for nonadiabatic couplings is introduced, based on the potentials, their gradients and Hessians. As deep-learning method, we employ SchNet extended for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules and paves the way towards efficient photodynamics simulations of complex systems.

本研究采用SchNet和SHARC相结合的方法，运用深度学习在光化学领域进行研究，同时学习了多能级、力学量、非绝热耦合和自旋轨道耦合等重要性质，其中，对于非绝热耦合的学习方式更是以相位无关的形态实现，并引入了基于势、势梯度和海森矩阵的非绝热耦合拟合方法。经过在理想系统和两种实际多原子分子测试，该方法被证明可行，为复杂系统的高效光动力学模拟铺平了道路。

结合SchNet和SHARC的SchNarc机器学习方法用于激发态动力学