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Rational design and glass-forming ability prediction of bulk metallic glasses via interpretable machine learning

Published Online: 2023-05-19

Published Print: 2023-06

Authors

Long, Tao

Long, Zhilin https://orcid.org/0000-0001-5491-7525
Peng, Zheng
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (51971188)
License Information

Text and Data Mining valid from 2023-05-19

Version of Record valid from 2023-05-19
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Article History

Received: 17 March 2023

Accepted: 22 April 2023

First Online: 19 May 2023

Declarations

:

: The authors declare that they have no conflict of interest.

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