Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

Author:

Sarker Suchismita1,Tang-Kong Robert1,Schoeppner Rachel23,Ward Logan4ORCID,Hasan Naila Al5ORCID,Van Campen Douglas G.1,Takeuchi Ichiro4,Hattrick-Simpers Jason6ORCID,Zakutayev Andriy3ORCID,Packard Corinne E.23ORCID,Mehta Apurva1ORCID

Affiliation:

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

2. Department of Metallurgy and Materials Engineering, Colorado School of Mines, Golden, Colorado 80401, USA

3. National Renewable Energy Laboratory, Golden, Colorado 80401, USA

4. Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

5. Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, USA

6. National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA

Funder

U.S. Department of Energy

National Renewable Energy Laboratory

Publisher

AIP Publishing

Subject

General Physics and Astronomy

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