Capturing Excited State Proton Transfer Dynamics with Reactive Machine Learning Potentials

Excited state proton transfer is a fundamental process in photochemistry, playing a crucial role in fluorescence sensing, bioimaging, and optoelectronic applications. However, fully resolving its dynamics remains challenging due to the prohibitive computational cost of <i>ab initio</i> simulations and the need for ultrafast experimental techniques with high temporal resolution. Here, we tackle this challenge by using machine learning-driven excited state molecular dynamics simulations. We propose an active learning framework powered by enhanced sampling techniques for constructing a high-quality training set for excited state machine learning potentials, which we then use to map the reaction free energy landscape and capture the photorelaxation dynamics. Using 10-hydroxybenzo[<i>h</i>]quinoline as a test case, our simulations reveal a barrierless excited state proton transfer occurring within ∼50 fs, accompanied by a significant red shift in the emission energy (∼1 eV), in agreement with experimental findings. Furthermore, our results highlight a strong coupling between proton transfer and charge redistribution, which facilitates the rapid tautomerization process. These findings showcase the power of machine learning-driven molecular dynamics in accurately capturing photochemical dynamics while enabling large-scale statistical sampling.