Efficient first principles based modeling <i>via</i> machine learning: from simple representations to high entropy materials-Reference-Cited by-同舟云学术

Efficient first principles based modeling via machine learning: from simple representations to high entropy materials

Published:2024 Issue:21 Volume:12 Page:12412-12422
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Li Kangming¹^ORCID,Choudhary Kamal²^ORCID,DeCost Brian²^ORCID,Greenwood Michael³,Hattrick-Simpers Jason¹⁴⁵⁶^ORCID

Affiliation:

1. Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada

2. Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, USA

3. Canmet MATERIALS, Natural Resources Canada, 183 Longwood Road south, Hamilton, ON, Canada

4. Acceleration Consortium, University of Toronto, 80 St George St, Toronto, ON M5S 3H6, Canada

5. Vector Institute for Artificial Intelligence, 661 University Ave, Toronto, ON, Canada

6. Schwartz Reisman Institute for Technology and Society, 101 College St, Toronto, ON, Canada

Abstract

Generalization performance of machine learning models: (upper panel) generalization from small ordered to large disordered structures (SQS); (lower panel) generalization from low-order to high-order systems.

Funder

Canada First Research Excellence Fund

Office of Energy Research and Development

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D4TA00982G

Reference74 articles.

1. Breakthrough applications of high-entropy materials

2. A critical review of high entropy alloys and related concepts

3. High-entropy alloys

4. High-entropy energy materials: challenges and new opportunities

5. Recent progress of high-entropy materials for energy storage and conversion

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