A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies-Reference-Cited by-同舟云学术

A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies

Published:2022 Issue:2 Volume:10 Page:872-880
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Li Xin¹,Li Bo¹,Yang Ze¹^ORCID,Chen Zhiwen²,Gao Wang¹^ORCID,Jiang Qing¹^ORCID

Affiliation:

1. Key Laboratory of Automobile Materials, Ministry of Education, Department of Materials Science and Engineering, Jilin University, 130022, Changchun, China

2. Department of Materials Science and Engineering, University of Toronto, Toronto, ON, M5S, 3E4, Canada

Abstract

We propose a transferable machine-learning model based on the intrinsic descriptors, which can predict the adsorption energies of single-atom alloys, AB intermetallics and high-entropy alloys , simply by training the properties of transition metals.

Funder

State Key Laboratory of High Performance Ceramics and Superfine Microstructure

National Natural Science Foundation of China

People's Government of Jilin Province

Jilin University

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TA/D1TA09184K

Reference58 articles.