Scientific Sessions

Computational and Theoretical Catalysis is a rapidly evolving field that leverages the power of advanced computational techniques and theoretical models to design and understand catalytic systems at the molecular level. By integrating principles from quantum chemistry, molecular dynamics, and statistical mechanics, this domain enables researchers to predict the behavior of catalysts, investigate reaction mechanisms, and identify active sites with high precision. These insights are crucial for the rational design of more efficient, selective, and environmentally friendly catalysts, which are vital in diverse industrial processes such as energy conversion, chemical manufacturing, and environmental remediation. Theoretical simulations not only reduce the need for extensive trial-and-error experimentation but also accelerate the discovery of new catalytic materials.

Recent advancements in high-performance computing and machine learning have further expanded the scope of computational catalysis. Researchers can now analyze large datasets and perform high-throughput virtual screening of catalyst candidates, which significantly enhances the speed and scale of catalyst development. Computational methods also facilitate the exploration of reaction pathways that are difficult to access experimentally, providing a deeper understanding of reaction kinetics and thermodynamics. With growing demands for sustainable chemical processes, computational and theoretical catalysis plays a pivotal role in the transition towards green chemistry. It fosters innovation in areas such as CO2 reduction, hydrogen production, and biomass conversion, making it an indispensable tool in modern catalysis research and application.