An element-wise machine learning strategy to predict glass-forming range of ternary alloys enabled by comprehensive data
Author:
Publisher
Elsevier BV
Subject
Condensed Matter Physics,General Materials Science,Mechanics of Materials,Metals and Alloys,Mechanical Engineering
Reference23 articles.
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4. Bulk Glass-forming metallic alloys: science and technology;Johnson;MRS Bull.,1999
5. Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases;Perim;Nat. Commun.,2016
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Efficient learning strategy for predicting glass forming ability in imbalanced datasets of bulk metallic glasses;Physical Review Materials;2024-05-10
2. Relating the combinatorial materials chip mapping to the glass-forming ability of bulk metallic glasses via diffraction peak width;Scripta Materialia;2024-02
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