Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials

TL;DR

This paper introduces a machine learning-based interatomic potential to efficiently simulate excited-state nonadiabatic dynamics in explicit solvent, accurately replicating quantum mechanics/molecular mechanics results while reducing computational costs.

Contribution

The authors develop and validate a machine-learned interatomic potential that replaces traditional QM/MM electrostatic embedding in nonadiabatic dynamics simulations, enabling faster and accurate trajectory calculations.

Findings

ML/MM model reproduces electronic kinetics of QM/MM simulations.

The approach accurately captures structural rearrangements in excited states.

Performance metrics for validating ML/MM models are established.

Abstract

Excited-state nonadiabatic simulations with quantum mechanics/molecular mechanics (QM/MM) are essential to understand photoinduced processes in explicit environments. However, the high computational cost of the underlying quantum chemical calculations limits its application in combination with trajectory surface hopping methods. Here, we use FieldSchNet, a machine-learned interatomic potential capable of incorporating electric field effects into the electronic states, to replace traditional QM/MM electrostatic embedding with its ML/MM counterpart for nonadiabatic excited state trajectories. The developed method is applied to furan in water, including five coupled singlet states. Our results demonstrate that with sufficiently curated training data, the ML/MM model reproduces the electronic kinetics and structural rearrangements of QM/MM surface hopping reference simulations. Furthermore, we identify performance metrics that provide robust and interpretable validation of model accuracy.