Faculty and Undergraduate Student Research Database

Catalysis is used to produce most chemicals worldwide. Thus, optimization of catalysts is relevant for both economic and environmental reasons. The ever-increasing computational power has led to the rise of computational research in catalysis that has been one of the main developments of the previous decades in the field. Computations have helped understanding chemical bonding, assign spectroscopic features, and explore reaction mechanisms among others. Regarding this latter, identifying rate-determining steps and analyzing critical chemical interactions have become standard tools to understand catalytic reactions and design more active, selective, and/or stable catalysts. As such, the Szilvasi group is interested in using computational met...

Designing new environmentally friendly and cheap materials for practical applications is one of the main challenges of our century. This process is however very slow because synthesizing and testing new materials take time and have considerable cost. Computational methods provide an alternative method to screen materials faster and circumvent the costly and slow experimental trial-and-error approach. In this research area, machine learning-based methods have emerged as flexible tools recently to predict the properties of hitherto unknown materials based on previously known information. The Szilvasi group is working on developing databases and machine learning-based workflows to design new materials in the area of catalysis, energy storage, ...