A catalogue of metallic glass-forming alloy systems

被引：0
作者：
Xie, Weijie [1 ,2 ]
Li, Mingxing [1 ]
Sun, Yitao [1 ]
Wang, Chao [1 ]
Hu, Liwei [1 ]
Liu, Yanhui [1 ,3 ]
机构：
[1] Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China
[3] Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China
来源：
MATERIALIA | 2025年 / 39卷
基金：
中国国家自然科学基金;
关键词：
Metallic glasses; Machine learning; Combinatorial method; Glass forming ability; AMORPHOUS-ALLOYS; LIQUID; CLASSIFICATION;
D O I：
10.1016/j.mtla.2025.102375
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Rational materials design out of vast compositional space is attractive yet challenging. The data-driven approach has shown promise in accelerating the development of advanced multicomponent alloys, such as metallic glasses. However, data-driven development of glass-forming alloys is limited by the sparse and biased datasets. In this study, we establish the high-throughput experimental database (HED), featuring an unprecedented quantity and diversity of experimental data. This database, encompassing 15,080 materials from 33 alloy systems synthesized and characterized under consistent conditions, provides a robust dataset for the training of machine learning model. The developed model is validated by both literature data and high-throughput experiments, and enables the creation of a catalogue of metallic glass forming alloy systems. The catalogue would serve as a practical reference for efficient design of glass-forming alloys systems.
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收藏
页数：6
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