机器学习在非晶合金逆向设计中的应用

doi:10.11896/cldb.25020053

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Abstract The inverse design paradigm provides a revolutionary idea for breaking through the conventional material research and development model by constructing a reverse mapping relationship from “performance requirements” back to “material structure” and even “composition”. Amorphous alloys have garnered considerable attention in materials science research due to their excellent mechanical properties and promising practical value. However, the characteristic long-range disordered atomic arrangement and the complex structure-property relationships make it highly challenging to achieve performance-oriented precise design using conventional trial-and-error methods. Machine learning technology integrates high-throughput computation, experimental databases, and intelligent algorithms to construct predictive models mapping multi-dimensional features to target properties, thereby significantly enhancing both the efficiency and reliability of alloy composition design. To enhance the understanding of this emerging interdisciplinary field, this summary systematically reviews the recent progress of machine learning in the inverse design of amorphous alloys. Firstly, the framework of machine learning-driven material design is briefly introduced. Secondly, the synergistic mechanism between high-throughput experiments and active learning in identifying key features, as well as the application of global optimization strategies in balancing multi-objective properties, is discussed. Thirdly, the unique advantages of generative models in exploring high-dimensional composition spaces are elaborated in detail. Finally, the achievements and challenges in the inverse design of amorphous alloys are summarized, perspectives on future research directions are provided. This paper aims to provide a systematic knowledge framework for the design of amorphous alloys, thereby accelerating the development of next-generation material development systems and application expansion.

Key words： machine learning amorphous alloy performance prediction reverse design

Published: 25 February 2026 Online: 2026-02-13

CLC number:	TB31
	TP18

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	LONG Zhuo
	FU Yifan
	YANG Gongji

Cite this article:

LONG Zhuo,FU Yifan,YANG Gongji. Application of Machine Learning in Reverse Design of Amorphous Alloys[J]. Materials Reports, 2026, 40(4): 25020053-9.

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https://www.mater-rep.com/EN/10.11896/cldb.25020053 OR https://www.mater-rep.com/EN/Y2026/V40/I4/25020053

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