CMDS Laboratory

for Energy & Environmental Research

I am developing multiscale and AI-enabled modeling approaches to understand and design heterogeneous catalysis and battery materials. My work integrates first principles simulations with machine learning to extract mechanistic insight and to build predictive, interpretable models that remain anchored to physics. In particular, I focus on how realistic interfacial environments such as solvation, surface coverage, defects, and dynamic restructuring reshape reaction energetics and kinetics, and how these effects ultimately control activity, selectivity, and stability. Further, I collaborate closely with experimental teams to translate computational results into measurable, design-relevant guidance, including testable hypotheses, prioritized material targets, and operating condition trends.