Accelerating Catalyst Discovery Using General Datasets and Graph Neural Networks
Zachary Ulissi a
a Carnegie Mellon University, Department of Materials Science and Engineering, Pittsburgh, 0, United States
Materials for Sustainable Development Conference (MATSUS)
Proceedings of nanoGe Spring Meeting 2022 (NSM22)
#AdvMatSyn22. Advanced Materials Synthesis, Characterization, and Theory: for the Green Energy Leap
Online, Spain, 2022 March 7th - 11th
Organizer: Francesca Toma
Invited Speaker, Zachary Ulissi, presentation 145
DOI: https://doi.org/10.29363/nanoge.nsm.2022.145
Publication date: 7th February 2022

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. 

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