Chemical Engineering 590STC - Atomistic Simulations

Heterogeneous catalysis plays an important role in industrial processes, including energy conversion, chemical production, and environmental remediation. In recent decades, computational modeling has made remarkable progress towards understanding the nature of active sites and elementary reaction steps. Despite these advancements in the field, there is still an existing gap between theory and experiment, which hinders the rapid discovery of efficient catalytic materials. This discrepancy arises from the complexity of the solid-liquid interfaces where electrocatalytic reactions take place. However, predicting and controlling these interfaces remain a grand challenge due to their structural and chemical complexities, especially under working conditions. The interplay between electronic structure, solvation effects, applied potential, and reaction intermediates introduces additional challenges in accurately modeling catalytic behavior. Developing predictive models that can capture these effects is crucial for accelerating the discovery of the next-generation energy materials. This course will provide students with a strong theoretical foundation in atomistic modeling techniques, including density functional theory (DFT), molecular dynamics, and machine learning (ML)-based approaches. Through a combination of lectures and hands-on computational exercises, students will explore how these methods are applied to heterogeneous catalysis, from elucidating reaction mechanisms to designing more efficient catalysts.

Open to all juniors and seniors in Engineering, Physics, Chemistry, and Biochemistry & Molecular Biology, and all graduate students.