PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

arXiv:2211.12020v4 Announce Type: replace
Abstract: Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst …

arxiv catalyst cs.lg design gnns physics physics.comp-ph scalable type