Skip to main content

Machine learning for alloys

Publication ,  Journal Article
Hart, GLW; Mueller, T; Toher, C; Curtarolo, S
Published in: Nature Reviews Materials
August 1, 2021

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and enhanced data generation have created a fertile ground for computational materials science. Pairing machine learning and alloys has proven to be particularly instrumental in pushing progress in a wide variety of materials, including metallic glasses, high-entropy alloys, shape-memory alloys, magnets, superalloys, catalysts and structural materials. This Review examines the present state of machine-learning-driven alloy research, discusses the approaches and applications in the field and summarizes theoretical predictions and experimental validations. We foresee that the partnership between machine learning and alloys will lead to the design of new and improved systems.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Nature Reviews Materials

DOI

EISSN

2058-8437

Publication Date

August 1, 2021

Volume

6

Issue

8

Start / End Page

730 / 755

Related Subject Headings

  • 4016 Materials engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021). Machine learning for alloys. Nature Reviews Materials, 6(8), 730–755. https://doi.org/10.1038/s41578-021-00340-w
Hart, G. L. W., T. Mueller, C. Toher, and S. Curtarolo. “Machine learning for alloys.” Nature Reviews Materials 6, no. 8 (August 1, 2021): 730–55. https://doi.org/10.1038/s41578-021-00340-w.
Hart GLW, Mueller T, Toher C, Curtarolo S. Machine learning for alloys. Nature Reviews Materials. 2021 Aug 1;6(8):730–55.
Hart, G. L. W., et al. “Machine learning for alloys.” Nature Reviews Materials, vol. 6, no. 8, Aug. 2021, pp. 730–55. Scopus, doi:10.1038/s41578-021-00340-w.
Hart GLW, Mueller T, Toher C, Curtarolo S. Machine learning for alloys. Nature Reviews Materials. 2021 Aug 1;6(8):730–755.

Published In

Nature Reviews Materials

DOI

EISSN

2058-8437

Publication Date

August 1, 2021

Volume

6

Issue

8

Start / End Page

730 / 755

Related Subject Headings

  • 4016 Materials engineering