Journal
MRS COMMUNICATIONS
Volume 9, Issue 2, Pages 576-585Publisher
SPRINGER HEIDELBERG
DOI: 10.1557/mrc.2019.44
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Funding
- National Key R&D Program of China [2018YFB0704404]
- Hong Kong Polytechnic University [1-ZE8R, G-YBDH]
- 111 Project of the State Administration of Foreign Experts Affairs
- Ministry of Education, China [D16002]
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There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.
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