PICS Colloquium: Combining High-Throughput Workflows, Quantum Chemistry, and AI for the Discovery of Tunable Materials with Unprecedented Properties with

Speaker: Andrew Rosen is an Assistant Professor of Chemical and Biological Engineering at Princeton University. His research group leverages recent advances in high-throughput computing, machine learning, and quantum-chemical calculations to predictively design new materials for a more sustainable future.

Abstract: The solutions to many of society’s most pressing problems rely on the discovery of materials with unprecedented physical and chemical properties that are tailored to an application of interest. Typically, it is not a matter of incremental improvements over existing technologies; rather, there is often an urgent need to identify new kinds of materials altogether. In this talk, I will discuss how quantum chemistry, high-throughput computing, and machine learning can help guide the discovery of novel, energy-relevant materials. I will highlight several representative success stories for this approach as well as potential shortcomings, using the areas of chemical separations and catalysis as demonstrative topics. I will also briefly highlight the open-source software and community science efforts I have contributed to that enables us to leverage the full capabilities of modern high-performance computing resources for materials discovery problems.

Here is a Zoom link for those who cannot join in person!