Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments-Reference-Cited by-同舟云学术

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

Author:

Ren Fang¹^ORCID,Ward Logan²³^ORCID,Williams Travis⁴^ORCID,Laws Kevin J.⁵^ORCID,Wolverton Christopher²,Hattrick-Simpers Jason⁶,Mehta Apurva¹

Affiliation:

1. Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.

2. Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.

3. Computation Institute, University of Chicago, Chicago, IL 60637, USA.

4. College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA.

5. School of Materials Science and Engineering, UNSW, Sydney, New South Wales 2052, Australia.

6. Materials for Energy and Sustainable Development Group, National Institute of Standards and Technology, MS 8520, Gaithersburg, MD 20899, USA.

Abstract

Coupling artificial intelligence with high-throughput experimentation accelerates discovery of amorphous alloys.

Funder

National Science Foundation

U.S. Department of Energy

U.S. Department of Commerce

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference39 articles.

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