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Pourbaix Machine Learning Framework Identifies...
Journal article

Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution

Abstract

The demand for green hydrogen has raised concerns over the availability of iridium used in oxygen evolution reaction catalysts. We identify catalysts with the aid of a machine learning-aided computational pipeline trained on more than 36,000 mixed metal oxides. The pipeline accurately predicts Pourbaix decomposition energy (Gpbx) from unrelaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic …

Authors

Abed J; Heras-Domingo J; Sanspeur RY; Luo M; Alnoush W; Meira DM; Wang H; Wang J; Zhou J; Zhou D

Journal

Journal of the American Chemical Society, Vol. 146, No. 23, pp. 15740–15750

Publisher

American Chemical Society (ACS)

Publication Date

June 12, 2024

DOI

10.1021/jacs.4c01353

ISSN

0002-7863

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