pubs.acs.org
MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics
Photochemical processes span time scales from femtoseconds to nanoseconds, and their simulation via fewest-switches surface hopping (FSSH) requires a large number of computationally expensive electronic structure evaluations. Machine learning (ML) interatomic potentials can reduce this cost; however, they must be trained on data sets that capture the most relevant regions of configurational space. We present MELTS, a fully automated active learning (AL) program for FSSH that iteratively improves ML models by using trajectory propagation to guide sampling. MELTS integrates Newton-X and MLatom through socket-based communication, minimizing I/O overhead and enabling large-scale simulations with a user-friendly interface. We validate the AL protocol implemented in MELTS on two contrasting systems: ultrafast fulvene dynamics (tens of femtoseconds) and nanosecond-scale pyrene fluorescence. In both cases, MELTS delivers quantitative agreement with reference quantum results while reducing computational time by up to 3 orders of magnitude. This demonstrates that MELTS can efficiently generate accurate ML potentials for photochemical processes across a wide range of time scales.
