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.

引用

页数：6

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