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Identifying key features for predicting glass-forming ability of bulk metallic glasses via interpretable machine learning

Published Online: 2024-05-09

Published Print: 2024-05

Authors

Zeng, Yangchuan

Tian, Zean https://orcid.org/0000-0002-7037-9939
Zheng, Quan

Bu, Anguo

Xie, Quan
Funding

Funding for this research was provided by:

National Key R&D Program of China (2021YFB0300102)

National Natural Science Foundation of China (62376091, 51661005, U1612442)
License Information

Text and Data Mining valid from 2024-05-01

Version of Record valid from 2024-05-01
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Article History

Received: 11 January 2024

Accepted: 14 April 2024

First Online: 9 May 2024

Declarations

:

: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

: This article does not contain any studies with human participants or animals performed by any of the authors.

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