Scientific Sessions

Computational catalysis and reaction modeling utilize theoretical and computational tools to understand, predict, and optimize catalytic processes at the molecular level. By applying quantum chemistry, density functional theory (DFT), molecular dynamics, and kinetic Monte Carlo simulations, researchers can investigate reaction mechanisms, energy profiles, and transition states without relying solely on experimental trial-and-error. This approach enables the rational design of catalysts with tailored activity, selectivity, and stability, reducing development time and cost. Computational models also help identify active sites, reaction intermediates, and rate determining steps, providing insights that guide experimental synthesis and testing of new catalytic materials.

Reaction modeling integrates computational insights with chemical kinetics and reactor engineering to predict the behavior of catalytic systems under various operational conditions. Models can simulate reactor performance, mass and heat transfer effects, and scaling-up processes, assisting in the design of efficient industrial reactors. Advanced techniques like multi scale modeling and machine learning algorithms are increasingly used to accelerate catalyst discovery, optimize reaction pathways, and improve sustainability in chemical manufacturing. Overall, computational catalysis and reaction modeling bridge theoretical understanding and practical applications, enabling smarter, faster, and more efficient catalytic process development.