We are an interdisciplinary computational & AI-for-Science research group in the Department of Materials Design and Innovation at the University at Buffalo. We develop physics-grounded AI and atomistic modeling to understand how local chemistry, complex material states, and experimental evidence jointly determine function in catalytic and energy systems. We aim to train students and postdoctoral researchers who can work fluently across these traditional disciplinary boundaries and develop new approaches at the intersection of chemical/materials science and AI.
Our research asks how reactive interfaces evolve under operating conditions, how chemical disorder and synthesis determine accessible material states, and how experimental evidence and uncertainty can guide scientific discovery. Rather than using AI simply for faster prediction, we seek models that identify physically plausible states, reveal chemical mechanisms, and help determine what to calculate, measure, or explore next. Across these directions, we combine first-principles modeling, statistical mechanics, thermodynamic/kinetic principles, scientific machine learning, and uncertainty-aware decision-making in close collaboration with experimental researchers:
- Reactive Catalytic Interfaces: How do local bonding, electronic structure, and evolving active-site ensembles control chemical reactivity/stability under operating conditions?
- Disordered Energy Materials: How do chemical order and disorder, synthesis, and configurational complexity determine which material states form and how they function?
- Closed-Loop Agentic Discovery: How can multimodal experimental evidence and uncertainty be converted into mechanistic interpretations and better scientific decisions?
We gratefully acknowledge funding and computing support from:
