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

Author(s)
Julia Westermayr, Michael Gastegger, Philipp Marquetand
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 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.

Organisation(s)
Department of Theoretical Chemistry, Research Platform Accelerating Photoreaction Discovery, Research Network Data Science
External organisation(s)
Technische Universität Berlin
Journal
Journal of Physical Chemistry Letters
Volume
11
Pages
3828-3834
No. of pages
7
ISSN
1948-7185
DOI
https://doi.org/10.1021/acs.jpclett.0c00527
Publication date
05-2020
Peer reviewed
Yes
Austrian Fields of Science 2012
104017 Physical chemistry, 103006 Chemical physics
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/combining-schnet-and-sharc-the-schnarc-machine-learning-approach-for-excitedstate-dynamics(a9074818-0553-47f3-af5a-7f3babea1f89).html