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

Researchers

Journal

Modalities

Models

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 pre-processing 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) incorporate spin-orbit couplings. As 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 towards efficient photodynamics simulations of complex systems.