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Engineering Under Extreme Conditions
We develop and apply artificial intelligence (AI) -driven simulation and inference tools to:
Understand fundamental behavior of matter under extreme conditions, and exploit new insights for in silico design of next generation materials
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.
Establish an atomistically-resolved view of inherently multiscaled reacting systems to enable optimization of advanced synthesis and manufacturing technologies
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.