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

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.