Computational Catalysis and Emerging Materials Laboratory

The Computational Catalysis and Emerging Materials Laboratory (CCEL) develops next-generation catalysts and energy materials by integrating artificial intelligence, atomistic simulations, and experimental validation. Our research combines density functional theory (DFT), molecular dynamics (MD), machine-learning interatomic potentials (MLIPs), microkinetic modeling, and data-driven approaches to establish fundamental design principles governing catalytic activity, selectivity, and durability. Our ultimate goal is to accelerate materials discovery for sustainable energy conversion, carbon neutrality, and environmentally responsible chemical manufacturing. We develop machine-learning and foundation-model-based frameworks that dramatically accelerate catalyst and materials discovery. Our research focuses on machine-learning interatomic potentials, active-learning workflows, catalyst foundation models, and automated screening platforms that bridge first-principles accuracy with realistic catalyst complexity. We investigate atomically dispersed catalytic sites for electrochemical and thermochemical reactions. By controlling coordination environments, electronic structures, spin states, and metal-support interactions, we establish atomic-level design principles for highly active, selective, and durable catalysts. Defects, lattice oxygen, and interfaces play crucial roles in catalytic performance. We study how oxygen vacancies, strain, segregation, and metal exsolution influence catalytic activity and stability. Recent efforts focus on hierarchical oxide architectures and dynamically evolving catalytic interfaces under realistic reaction conditions. We design catalysts and electrode materials for water electrolysis, seawater electrolysis, CO2 conversion, fuel cells, and revers