"Computational Approaches to Theory and Experiment in Chemical Catalysis"
Mar 9, 2022 Schedule:
- Tea Time - Virtual ( Zoom)
- 03:00 to 03:30 PM Eastern Time (US and Canada)
- Colloquium - F2F ( 499 DSL) / Virtual ( Zoom)
- 03:30 to 04:30 PM Eastern Time (US and Canada)
It has been a long-term goal for nanoscience research to integrate experiment, theory, and computational approaches. While predictive models from theory and computation are still rare, the goal of integration is becoming closer because of the rapid progress in artificial intelligence and machine learning. Using two examples from our current work in catalysis, I will discuss how computation is used together with experiment and theory. In the first example, a data-driven machine learning approach is proposed, coupled with dynamic experiments, to do inverse chemical kinetics modeling. In the second example, a descriptor based on theoretical insight and ab initio calculations is introduced for the screening of semiconducting catalytic systems.