Atoms, algorithms, and systems at scale.

I develop computational methods that connect atomic-scale science with experimentally relevant materials questions. My work combines density functional theory, molecular dynamics, and machine learning to study molten salts, transition-metal complexes, composite interfaces, lubricants, and mechanically driven chemical reactions.

At Argonne National Laboratory, I develop machine-learned interatomic potentials and scientific machine-learning models. I also create software that makes complex simulation campaigns easier to run on leadership-class computers. This work supports electrochemical steelmaking, molecular screening, thermal-transport calculations, and data extraction from electrochemistry literature.

My Ph.D. research at UC Merced focused on mechanochemistry and tribology. I used reactive molecular dynamics to explain how shear stress changes reaction pathways at sliding interfaces. Across these projects, I work closely with experimental scientists and translate simulations, data, and software into testable physical insights.