期刊
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
卷 11, 期 10, 页码 3828-3834出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.0c00527
关键词
-
类别
资金
- University of Vienna
- European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant [792572]
- EC Research Innovation Action under the H2020 Programme [INFRAIA-2016-1-730897]
- NVIDIA
- AustrianScience Fund [W 1232]
- Marie Curie Actions (MSCA) [792572] Funding Source: Marie Curie Actions (MSCA)
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-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据