Machine Learning-Based Excited-State Reactive Force Field: A New Approach for Modeling the Photodissociation Dynamics of 2‑Fluorothiophenol

The absence of accurate yet efficient excited-state reactive
force
fields has emerged as a critical bottleneck hindering advancements
in photochemical dynamics. To overcome it, we develop a machine learning-based
excited-state reactive force field (ML-ES-RFF) that implements an
innovative divide-and-conquer strategy. Specifically, this approach
systematically classifies degrees of freedom (DOFs) into chemically
active and inactive ones based on their contributions in reactions.
For active DOFs, which exhibit a significant anharmonicity, the potential
energy surfaces are constructed using high-level quantum mechanics-based
neural network potentials (QM-NNPs). In contrast, for inactive DOFs,
which satisfy the harmonic approximation near equilibrium, the potential
energy surfaces could be described by conventional molecular mechanics
(MM) force fields. This multiscale methodology simultaneously achieves
quantum chemical accuracy and remarkable computational efficiency.
To validate the reliability of this approach, we applied it to study
the nonadiabatic dynamics of 2-fluorothiophenol using ML-ES-RFF, successfully
obtaining complete atomistic characterization of the photodissociation
process. Our framework establishes a new paradigm for studying excited-state
processes in complex molecular systems.