Machine Learning Based Electronic Structure Predictors in Single-Atom Alloys: A Model Study of CO Kink-Site Adsorption across Transition Metal Substrates-Reference-Cited by-同舟云学术

Machine Learning Based Electronic Structure Predictors in Single-Atom Alloys: A Model Study of CO Kink-Site Adsorption across Transition Metal Substrates

Published:2023-06-20 Issue:25 Volume:127 Page:12055-12067
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Shirani Javad¹,Pham Hanh D. M.²,Yuan Shuaishuai¹,Tchagang Alain B.³,Valdés Julio J.³,Bevan Kirk H.¹⁴^ORCID

Affiliation:

1. Division of Materials Engineering, Faculty of Engineering, McGill University, Montréal, Québec H3A 0C5, Canada

2. Department of Chemistry, McGill University, Montréal, Québec H3A 0B8, Canada

3. Digital Technologies Research Centre, National Research Council Canada, Ottawa, Ontario K1A 0R6, Canada

4. Centre for the Physics of Materials, Department of Physics, McGill University, Montréal, Québec H3A 2T8, Canada

Funder

National Research Council Canada

Faculty of Engineering, McGill University

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.3c02705

Reference88 articles.

1. Single-Atom Alloys as a Reductionist Approach to the Rational Design of Heterogeneous Catalysts

2. Isolated Metal Atom Geometries as a Strategy for Selective Heterogeneous Hydrogenations

3. Single-Atom Catalysts across the Periodic Table

4. Single-Atom Alloy Catalysis

5. Electrochemical ammonia synthesis via nitrate reduction on Fe single atom catalyst

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