On Friday, March 26 at 2:00 PICS will host a virtual a colloquium featuring Assistant Professor Zachary Ulissi of Carnegie Mellon University.
Title: Workflows, Datasets and Models for Active Discovery in Catalysis
Abstract: Machine learning accelerated catalyst discovery efforts has seen much progress in the last few years. Datasets of computational calculations have improved, models to connect surface structure with electronic structure or adsorption energies have gotten more sophisticated, and active learning exploration strategies are becoming routine in discovery efforts. However, there are several large challenges that remain: to date, models have had trouble generalizing to new materials or reaction intermediates and applying these methods requires significant training. I will review and discuss methods in my lab for high-throughput catalyst screening and on-line discovery of interesting materials, resulting in an optimized Cu-Al catalyst for CO2-to-ethylene conversion. I will then introduce the Open Catalyst Project and the Open Catalyst 2020 dataset, a collaborative project to span surface composition, structure, and chemistry and enable a new generation of deep machine learning models for catalysis, with initial results for state-of-the-art deep graph convolutional models. Finally, I will discuss on-going work to develop small ML models to accelerate routine calculations without requiring expert intervention.
Bio: Zachary Ulissi is an Assistant Professor of Chemical Engineering at Carnegie Mellon University. He works on the development and application of high-throughput computational methods in catalysis, machine learning models to predict their properties, and active learning methods to guide these systems. Applications include energy materials, CO2 utilization, fuel cell development, and additive manufacturing. He has been recognized nationally for his work including the 3M Non-Tenured Faculty Award and the AIChE 35-under-35 award among others.