AI Insight
This paper introduces NATPS (Nonadiabatic Transition Path Sampling), a new computational method designed to simulate rare nonadiabatic events in excited-state molecular dynamics. The approach combines a deterministic and time-reversible formulation of the Mapping Approach to Surface Hopping (MASH) with the established Transition Path Sampling (TPS) framework, satisfying the key conditions of time reversibility and detailed balance. Using a model system of coupled potential energy surfaces, the authors demonstrate that NATPS generates reactive trajectory ensembles more efficiently than brute-force simulations or forward-flux sampling.
Why it matters
Nonadiabatic processes are fundamental to photochemistry, including phenomena such as photodegradation, vision, and solar energy conversion, yet they are computationally prohibitive to study for rare events. NATPS offers a more tractable route to mechanistic insight into these processes, potentially accelerating the rational design of photochemical systems and light-harvesting materials.
arXiv:2603.08677v2 Announce Type: replace
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.
Source: NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics