机器学习技术在材料科学领域中的应用进展

doi:10.11896/cldb.20060168

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Abstract Materials are the foundation of the national economy, the discovery of new materials gives impetus to the development of modern science and technological innovation. The traditional “trial and error” experimental methods are no longer applicable to the research of modern materials owing to the disadvantages of high cost, long period and great contingency. In recent years, with the rapid development of artificial intelligence and data-driven approach, as a main branch and an important tool of them, machine learning is receiving increasing attention and showing tremendous potential. The integration of machine learning into material science research can greatly improve the precision and efficiency, and provide new opportunities for the research and development of new materials and the study of the basic theory.
Machine learning technology combines knowledge of computer science, probability theory, statistics, database theory and engineering. It shows a faster computing speed and good generalization ability, and can effectively deal with some systems and problems difficult to tackle by traditional experiments and numerical simulation. In the past decade, the applications of machine learning in material science research have shown explosive growth,especially in the synthesis and design of new materials, the property prediction, characterization of the microstructure, and the improvement of material calculation and simulation methods. Machine learning will be indispensable in the development of material science and engineering in the future. At present, how to obtain a large number of experimental data and build effective data set is still a hot spot and difficulty in the application of machine learning in the field of material science.
This paper outlines the basic principles, workflows and common algorithms of machine learning, briefly describes the research focus and application progress of machine learning technology in the field of materials science, and analyzes the existing problems of machine learning in mate-rials science research. Meanwhile, some hot spots of the material field in the future are pointed out.

Key words： machine learning performance prediction microstructure characterization calculation and simulation

Published: 31 August 2021

CLC number:

TP181

Fund:National Key Research and Development Program of China (2016YFB0301100), Natural Science Foundation of Chongqing (cstc2017jcyjBX0040), National Natural Science Foundation of China (51531002).

About author:: Xiaoxi Mi, a doctoral candidate, graduated from Chongqing University in 2018 with a master’s degree. Now he is a doctoral candidate in Chongqing University, with the guidance of Professor Aitao Tang. The major research is the application of machine learning in magnesium alloys.
Aitao Tang, Ph.D., professor, doctoral supervisor, key researchers of National Engineering research center for magnesium alloys. Focusing on magnesium alloy, aluminum alloy and composite materials. Mainly engaged in material database, material simulation and high performance materials research. In 1984, she graduated from the department of metallurgy of Chongqing University. In 2004, she received her Ph.D. degree at the School of Materials of Chongqing University. She served as the teaching staff of five undergraduate courses and one postgraduate course successively. She is the backbone teacher of the applied course of computer in material science and engineering. She has obtained a number of national authorized invention patents, published more than 60 important theses.
Fusheng Pan received his Ph.D. degree in Northwe-stern Polytechnical University. He is an academician of the Chinese academy of engineering, a professor of materials science at Chongqing University and a doctoral supervisor. In 1977, he joined the Jiusan Society, a member of the 11th CPPCC national committee, a member of the discipline evaluation group of the degree committee of the state council, and a member of the central committee of the Jiusan Society. He has served successively as lecturer, associate researcher, deputy director of the department, director of the institute, vice dean of the graduate school and director of the institute of light metals of Chongqing University. He has studied in Oxford University, Chiba University in Japan and national materials institute in Germany. He has published more than 180 papers and 7 books in important journals at home and abroad.

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Cite this article:

MI Xiaoxi,TANG Aitao,ZHU Yuchen, et al. Research Progress of Machine Learning in Material Science[J]. Materials Reports, 2021, 35(15): 15115-15124.

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http://www.mater-rep.com/EN/10.11896/cldb.20060168 OR http://www.mater-rep.com/EN/Y2021/V35/I15/15115

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