Zachary W. Ulissi, Assistant Professor of Chemical Engineering and Materials Science and Engineering, Carnegie Mellon University
Wednesday August 18th, 10am (USA/Pacific)
Machine learning accelerated catalyst discovery efforts have 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 briefly 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. I will then discuss initial results for state-of-the-art deep graph convolutional models and significant recent progress from others in the community, many of which are likely to improve models in related materials science areas. As an example application I will show how these efforts are already assisting in material development for water, in collaboration with Anubhav Jain (LBL).
A recording of this seminar is available here.
If you are unable to ask questions live, please feel welcome to ask any questions following the talk here and we will ask the speaker to check afterwards. Whether they will be able to answer questions or not depends on the speaker’s availability.