机器学习辅助合金理性设计研究进展

付华栋; 张洪涛; 王长胜; 雍维; 谢建新

doi:10.1007/s12613-022-2458-8

Volume 29 Issue 4

Apr. 2022

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文章导航 > 矿物冶金与材料学报（英文） > 2022 > 29(4): 635-644

Huadong Fu, Hongtao Zhang, Changsheng Wang, Wei Yong, and Jianxin Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 635-644. https://doi.org/10.1007/s12613-022-2458-8

Cite this article as:

Huadong Fu, Hongtao Zhang, Changsheng Wang, Wei Yong, and Jianxin Xie, Recent progress in the machine learning-assisted rational design of alloys, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 635-644. https://doi.org/10.1007/s12613-022-2458-8

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特约综述

机器学习辅助合金理性设计研究进展

1)
北京科技大学北京材料基因工程高精尖创新中心, 北京 100083, 中国
2)
北京科技大学材料先进制备技术教育部重点实验室, 北京 100083, 中国
3)
北京科技大学现代交通金属材料与加工技术北京实验室, 北京 100083, 中国

通讯作者:
谢建新 E-mail: jxxie@mater.ustb.edu.cn

文章亮点

(1) 介绍了机器学习辅助金属材料理性设计的基本策略。

(2) 综述了金属材料成分和工艺的逆向设计、选择设计和优化设计方法。

(3) 展望了机器学习辅助金属材料理性设计的未来发展趋势。

中文摘要

基于经验的传统“试错法”金属材料设计存在试错周期长、成本高等问题，大数据和人工智能技术的快速发展，为金属材料高效研发提供了新的途径—机器学习模型预测辅助材料设计。本文介绍了机器学习辅助金属材料理性设计的基本策略，重点综述了面向性能需求的合金成分逆向设计、基于合金元素物理化学特征或材料组织结构特征建模的合金成分选择设计、基于迭代反馈优化的合金成分和工艺参数优化设计三方面的研究进展，展望了机器学习辅助金属材料理性设计的未来发展趋势。

关键词:

Invited Review

Recent progress in the machine learning-assisted rational design of alloys

+ Author Affiliations

Abstract

Abstract

Alloys designed with the traditional trial and error method have encountered several problems, such as long trial cycles and high costs. The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials, that is, machine learning-assisted design. In this paper, the basic strategy for the machine learning-assisted rational design of alloys was introduced. Research progress in the property-oriented reversal design of alloy composition, the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors, and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed. Results showed the great advantages of machine learning, including high efficiency and low cost. Future development trends for the machine learning-assisted rational design of alloys were also discussed. Interpretable modeling, integrated modeling, high-throughput combination, multi-objective optimization, and innovative platform building were suggested as fields of great interest.
- machine learning;
- data mining;
- rational design;
- alloys

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