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

TLDR

Deep learning has become integral to everyday life and is revolutionizing quantum chemistry. This work shows how deep learning can advance photochemistry by learning all important properties—multiple energies, forces, and couplings—for photodynamics simulations. We simplify photodynamics simulations by phase‑free training, rotationally covariant nonadiabatic couplings (trained or approximated from ML potentials, gradients, and Hessians), incorporating spin‑orbit couplings, and extending SchNet to multiple electronic states. In combination with SHARC, our SchNarc approach was tested on two polyatomic molecules, demonstrating efficient photodynamics simulations of complex systems.

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.