Database of ternary amorphous alloys based on machine learning

doi:10.1088/1674-1056/ad8ec8

中国物理B ›› 2025, Vol. 34 ›› Issue (1): 16101-016101.doi: 10.1088/1674-1056/ad8ec8

Database of ternary amorphous alloys based on machine learning

Xuhe Gong(巩旭菏)^1,2, Ran Li(李然)^1,†, Ruijuan Xiao(肖睿娟)^2,‡, Tao Zhang(张涛)¹, and Hong Li(李泓)²

1 School of Materials Science and Engineering, Key Laboratory of Aerospace Materials and Performance (Ministry of Education), Beihang University, Beijing 100191, China;
2 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

收稿日期:2024-09-15 修回日期:2024-10-22 接受日期:2024-11-05 发布日期:2024-12-06
通讯作者: Ran Li, Ruijuan Xiao E-mail:liran@buaa.edu.cn;rjxiao@aphy.iphy.ac.cn
基金资助:
Project supported by funding from the National Natural Science Foundation of China (Grant Nos. 52172258, 52473227 and 52171150) and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB0500200).

Database of ternary amorphous alloys based on machine learning

Xuhe Gong(巩旭菏)^1,2, Ran Li(李然)^1,†, Ruijuan Xiao(肖睿娟)^2,‡, Tao Zhang(张涛)¹, and Hong Li(李泓)²

1 School of Materials Science and Engineering, Key Laboratory of Aerospace Materials and Performance (Ministry of Education), Beihang University, Beijing 100191, China;
2 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

Received:2024-09-15 Revised:2024-10-22 Accepted:2024-11-05 Published:2024-12-06
Contact: Ran Li, Ruijuan Xiao E-mail:liran@buaa.edu.cn;rjxiao@aphy.iphy.ac.cn
Supported by:
Project supported by funding from the National Natural Science Foundation of China (Grant Nos. 52172258, 52473227 and 52171150) and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB0500200).

1. 2025-016101-SI.pdf(539KB)

摘要/Abstract

摘要： The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties, rendering them highly promising for applications in catalysis, medicine, and battery technology, among other fields. Since not all materials can be synthesized into an amorphous structure, the composition design of amorphous materials holds significant importance. Machine learning offers a valuable alternative to traditional “trial-and-error” methods by predicting properties through experimental data, thus providing efficient guidance in material design. In this study, we develop a machine learning workflow to predict the critical casting diameter, glass transition temperature, and Young's modulus for 45 ternary reported amorphous alloy systems. The predicted results have been organized into a database, enabling direct retrieval of predicted values based on compositional information. Furthermore, the applications of high glass forming ability region screening for specified system, multi-property target system screening and high glass forming ability region search through iteration are also demonstrated. By utilizing machine learning predictions, researchers can effectively narrow the experimental scope and expedite the exploration of compositions.

关键词: amorphous alloys, machine learning, database

Abstract: The unique long-range disordered atomic arrangement inherent in amorphous materials endows them with a range of superior properties, rendering them highly promising for applications in catalysis, medicine, and battery technology, among other fields. Since not all materials can be synthesized into an amorphous structure, the composition design of amorphous materials holds significant importance. Machine learning offers a valuable alternative to traditional “trial-and-error” methods by predicting properties through experimental data, thus providing efficient guidance in material design. In this study, we develop a machine learning workflow to predict the critical casting diameter, glass transition temperature, and Young's modulus for 45 ternary reported amorphous alloy systems. The predicted results have been organized into a database, enabling direct retrieval of predicted values based on compositional information. Furthermore, the applications of high glass forming ability region screening for specified system, multi-property target system screening and high glass forming ability region search through iteration are also demonstrated. By utilizing machine learning predictions, researchers can effectively narrow the experimental scope and expedite the exploration of compositions.

Key words: amorphous alloys, machine learning, database

中图分类号: (Amorphous semiconductors, metals, and alloys)

61.43.Dq

71.23.Cq (Amorphous semiconductors, metallic glasses, glasses)

Xuhe Gong(巩旭菏), Ran Li(李然), Ruijuan Xiao(肖睿娟), Tao Zhang(张涛), and Hong Li(李泓). Database of ternary amorphous alloys based on machine learning[J]. 中国物理B, 2025, 34(1): 16101-016101.

Xuhe Gong(巩旭菏), Ran Li(李然), Ruijuan Xiao(肖睿娟), Tao Zhang(张涛), and Hong Li(李泓). Database of ternary amorphous alloys based on machine learning[J]. Chin. Phys. B, 2025, 34(1): 16101-016101.

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Database of ternary amorphous alloys based on machine learning

Database of ternary amorphous alloys based on machine learning

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