I build computational frameworks that connect atomic-scale mechanisms to measurable materials performance, combining atomistic simulation, rare-event sampling, and physics-informed machine learning.
I investigate battery electrodes and electrolytes to identify the atomic-scale mechanisms that govern performance, stability, and rate capability.
Recent efforts focus on sodium storage in disordered carbon, electrolyte transport at low temperatures, and interfacial ion dynamics that inform next-generation energy storage design.
I study aqueous reactivity and mineral-water interfaces to resolve the pathways that drive nucleation, dissolution, and interfacial transformation.
Using rare-event sampling and atomistic modeling, I quantify ion solvation and reaction free energies in systems relevant to environmental remediation, geochemical evolution, and carbon-management technologies.
I investigate material response under high pressure, high temperature, and high strain rate, with emphasis on phase transitions and defect evolution during shock loading.
By combining high-performance computing, theory, and machine-learning interatomic potentials, I model far-from-equilibrium behavior relevant to aerospace, national security, and energy applications.
I develop machine-learning models that preserve physical fidelity while enabling simulations at scales inaccessible to direct quantum methods.
Core efforts include neural-network interatomic potentials, transferable model development across chemical environments, and structure-generation workflows that accelerate mechanism discovery and model-driven design.
My methodology combines complementary simulation and data-driven tools to connect atomistic mechanisms with experimentally relevant behavior:
In close collaboration with experimental teams, I use these models to interpret observations, test mechanistic hypotheses, and prioritize high-impact material directions.