Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics.

ABSTRACT: 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

SUBMITTER: Westermayr J 

PROVIDER: S-EPMC7246974 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature