Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions-Reference-Cited by-同舟云学术

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

Published:2024 Issue:13 Volume:14 Page:3784-3799
ISSN:2044-4753
Container-title:Catalysis Science & Technology
language:en
Short-container-title:Catal. Sci. Technol.

Author:

Martínez-Alonso Carmen¹²³^ORCID,Vassilev-Galindo Valentin¹^ORCID,Comer Benjamin M.³,Abild-Pedersen Frank³,Winther Kirsten T.³,LLorca Javier¹⁴^ORCID

Affiliation:

1. IMDEA Materials Institute, C/Eric Kandel 2, 28906 – Getafe, Madrid, Spain

2. Department of Inorganic Chemistry, Complutense University of Madrid, 28040 Madrid, Spain

3. SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, Stanford, California 94305, USA

4. Department of Materials Science, Polytechnic University of Madrid, E. T. S. de Ingenieros de Caminos, 28040 Madrid, Spain

Abstract

A machine learning algorithm was trained with a DFT database of H, O, and OH adsorption energies into pure metals and bimetallic compounds including the effect of elastic strains to discover new intermetallic catalysts for the HER and the ORR.

Funder

HORIZON EUROPE Marie Sklodowska-Curie Actions

Agencia Estatal de Investigación

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/CY/D4CY00491D

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