NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics

TL;DR

This paper introduces NATPS, a novel method combining nonadiabatic MASH dynamics with transition path sampling, enabling efficient simulation of rare excited-state events in photochemistry.

Contribution

It develops a deterministic, time-reversible nonadiabatic dynamics framework compatible with transition path sampling, improving efficiency over traditional methods.

Findings

NATPS efficiently generates reactive trajectory ensembles.

It provides mechanistic insights into nonadiabatic pathways.

Reduces computational effort compared to brute-force simulations.

Abstract

Rare nonadiabatic events play a central role in photochemistry but remain difficult to simulate because excited-state dynamics is computationally demanding and often stochastic. Here we introduce a deterministic and time-reversible implementation of nonadiabatic dynamics that enables the application of transition path sampling (TPS) to excited-state processes. Our approach builds on the Mapping Approach to Surface Hopping (MASH) and establishes the conditions required for path ensemble sampling, in particular time reversibility and detailed balance. Combining this dynamics with the TPS framework yields a new method, termed nonadiabatic transition path sampling (NATPS). Using a model system of electronically coupled potential energy surfaces, we demonstrate that NATPS efficiently generates ensembles of reactive trajectories and provides mechanistic insight into nonadiabatic pathways. Compared with brute-force trajectory simulations and forward-flux sampling approaches, NATPS substantially reduces the computational effort required to obtain reactive trajectories.