Chemistry occurring under extreme temperature, pressure, and strain rate conditions has implications for a myriad of applications spanning advanced material processing techniques (e.g., laser ablation, sonication and electron-beam modification), defense applications (e.g., detonation science), sustainable energy (e.g., nuclear power), and beyond (e.g., geology, astrophysics, and astrobiology). Our group employs AI-driven multiscale simulations to facilitate advances in these areas.

Material and process optimization (e.g., for hydrogen storage, carbon sequestration, and catalysis) is frequently complicated by complex governing phenomena and massive design manifolds. We accelerate these efforts through atomistic simulation-informed machine learning strategies, i.e., using simulations to elucidate governing phenomena and identify regions of interest, and machine learning to identify optimal solutions within the resulting design spaces.