Scientific Sessions

Computational catalysis is a rapidly advancing field that leverages theoretical models and high-performance computing to understand, predict, and design catalytic processes at the molecular level. By using quantum mechanical simulations, density functional theory (DFT), and molecular dynamics, researchers can analyze reaction mechanisms, transition states, energy barriers, and surface interactions without solely relying on costly and time-consuming experiments. This approach allows scientists to screen hundreds of catalysts virtually, identifying the most promising candidates for various chemical transformations, such as hydrogen production, carbon dioxide reduction, or ammonia synthesis. Computational insights not only accelerate the discovery of new materials but also guide the optimization of existing catalysts for improved efficiency and selectivity.

The impact of computational catalysis spans across industries, including energy, pharmaceuticals, and environmental technology. In green chemistry, for example, it supports the development of catalysts that minimize waste and reduce harmful byproducts. In fuel cell and battery research, it aids in identifying durable and cost-effective catalysts for sustainable energy conversion and storage. As machine learning and artificial intelligence become integrated with computational models, the predictive power and speed of catalyst discovery are significantly enhanced. Ultimately, computational catalysis plays a critical role in bridging the gap between fundamental science and real-world applications, fostering innovations that contribute to a cleaner, more efficient, and sustainable future in chemical engineering and materials science.