Applying Active Learning to the Screening of Molecular Oxygen Evolution Catalysts.

ABSTRACT: The oxygen evolution reaction (OER) can enable green hydrogen production; however, the state-of-the-art catalysts for this reaction are composed of prohibitively expensive materials. In addition, cheap catalysts have associated overpotentials that render the reaction inefficient. This impels the search to discover novel catalysts for this reaction computationally. In this communication, we present machine learning algorithms to enhance the hypothetical screening of molecular OER catalysts. By predicting calculated binding energies using Gaussian process regression (GPR) models and applying active learning schemes, we provide evidence that our algorithm can improve computational efficiency by guiding simulations towards candidates with promising OER descriptor values. Furthermore, we derive

SUBMITTER: Craig MJ 

PROVIDER: S-EPMC8588390 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature