Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features-Reference-Cited by-同舟云学术

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

Published:2022-03-30 Issue:7 Volume:34 Page:2945-2954
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Afflerbach Benjamin T.¹^ORCID,Francis Carter¹,Schultz Lane E.¹,Spethson Janine¹,Meschke Vanessa¹^ORCID,Strand Elliot¹,Ward Logan²^ORCID,Perepezko John H.¹,Thoma Dan¹,Voyles Paul M.¹^ORCID,Szlufarska Izabela¹^ORCID,Morgan Dane¹^ORCID

Affiliation:

1. University of Wisconsin-Madison, Madison, Wisconsin 53706-1595, United States

2. Argonne National Laboratory, Lemont, Illinois, 60439-4801, United States

Funder

U.S. Department of Energy

National Science Foundation

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.1c03542

Reference43 articles.

1. Bulk Metallic Glasses with Functional Physical Properties

2. Inoue, A.; Takeuchi, A., Bulk Metallic Glasses: Formation and Applications. Encyclopedia of Materials: Science and Technology, 2010; pp 1–6.

3. Stabilization of metallic supercooled liquid and bulk amorphous alloys

4. A general-purpose machine learning framework for predicting properties of inorganic materials

5. Thermodynamics and Kinetics of Bulk Metallic Glass

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