结合SchNet和SHARC：激发态动力学的SchNarc机器学习方法
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
摘要：
近年来，深度学习已经成为我们日常生活的一部分，并正在彻底改变量子化学。在这项工作中，我们展示了深度学习如何通过学习光动力学模拟的所有重要特性来推进光化学的研究领域。这些性质是多重能量、力、非绝热耦合和自旋轨道耦合。非绝热耦合以无相位的方式学习，作为深度学习模型虚拟构建的属性的导数，这保证了旋转协方差。此外，非绝热耦合的近似介绍，基于潜在的，他们的梯度和海森。作为深度学习方法，我们采用了扩展为多种电子状态的SchNet。结合分子动力学程序SHARC，我们的方法被称为SchNarc的模型系统和两个现实的多原子分子进行测试，并铺平了道路，有效的光动力学模拟的复杂系统。
英文摘要：
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.
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