Research Area

The development of sustainable and renewable energy technologies stands as a critical global challenge in the present era. Many of these clean energy generation technologies rely on catalytic processes and materials. Consequently, materials and catalysis play a pivotal role in generating valuable energy and fuels to meet the ever-increasing global energy demand driven by population growth and advancing civilization. Understanding the mechanisms of catalytic processes at a fundamental level is imperative for enhancing the efficiency of existing technologies. It is equally crucial for designing novel and high-performance catalytic materials that are cost-effective, abundant in nature, and environmentally friendly. The scientific methods and techniques developed in the field of catalysis and surface science over the last few decades, both experimental and theoretical, empower us to delve into atomic-level details in many industrially significant catalytic reactions. In particular, computational modeling methods possess predictive capabilities and have become mainstream tools in recent years for studying material properties at the nanoscale.

Our group at the University of Central Florida has been actively involved in the advancement and application of modeling techniques, spanning from electronic structure density functional theory (DFT) calculations to kinetic Monte Carlo (KMC) simulations/microkinetic modeling (MKM) and machine learning (ML). The primary objective of our research group is to utilize, develop, and refine cutting-edge computational methods to enhance our fundamental understanding of the structure-property relationship to design novel energetic materials. Simultaneously, we integrate ML/artificial intelligence (AI) with DFT and experimental data to establish a predictive platform for catalyst activity, selectivity, and degradation.