论文标题
结合Schnet和Sharc：激发状态动态的Schnarc机器学习方法
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
论文作者
论文摘要
近年来，深度学习已成为我们日常生活的一部分，并且正在彻底改变量子化学。在这项工作中，我们通过学习光动力学模拟的所有重要属性来展示如何使用深度学习来推进光化学的研究领域。这些性质是多个能量，力，非绝热耦合和自旋轨道耦合。非绝热耦合以无相的方式学习，作为通过深度学习模型虚拟构建特性的衍生物，这可以保证旋转协方差。此外，根据电势，梯度和黑森人，引入了非绝热耦合的近似值。作为深度学习方法，我们采用了用于多个电子状态的Schnet扩展。结合分子动力学程序Sharc，我们称为SCHNARC的方法在模型系统和两个逼真的多原子分子上进行了测试，并为复杂系统的有效光动力学模拟铺平了道路。
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.
