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材料导报  2021, Vol. 35 Issue (15): 15115-15124    https://doi.org/10.11896/cldb.20060168
  无机非金属及其复合材料 |
机器学习技术在材料科学领域中的应用进展
米晓希, 汤爱涛, 朱雨晨, 康靓, 潘复生
重庆大学材料科学与工程学院,重庆 400044
Research Progress of Machine Learning in Material Science
MI Xiaoxi, TANG Aitao, ZHU Yuchen, KANG Jing, PAN Fusheng
College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
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摘要 材料是国民经济的基础,新材料的发现是推动现代科学发展与技术革新的源动力之一,传统的实验“试错型”研究方法具有成本高、周期长和存在偶然性等特点,难以满足现代材料的研究需求。近些年,随着人工智能和数据驱动技术的飞速发展,机器学习作为其主要分支和重要工具,受到的关注日益增加,并在各学科领域展现出巨大的应用潜力。将机器学习技术与材料科学研究相结合,从大量实验与计算模拟产生的数据中挖掘信息,具有精度高、效率高等优势,给新材料的研发和材料基础理论的研究提供了新的契机。
机器学习技术结合了计算机科学、概率论、统计学、数据库理论以及工程学等知识,计算速度快、泛化能力强,能有效地处理一些难以运用传统实验及模拟计算方法解决的体系和问题。近10年,机器学习在材料科学研究中的应用呈现出爆炸式的增长,尤其在新材料的合成设计、性能预测、材料微观结构深入表征以及改进材料计算模拟方法几个方面,均有着出色的表现。当然,作为一项数据驱动技术,如何获取大量实验数据并将其构建为行之有效的数据集仍是现阶段机器学习技术在材料科学领域应用的热点和难点。
本文概述了机器学习技术的基本原理、主要工作流程和常用算法,简述了机器学习技术在材料科学领域中的研究重心及应用进展,分析了机器学习在材料学研究中尚存在的问题,并对未来此领域的发展热点进行了展望。
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米晓希
汤爱涛
朱雨晨
康靓
潘复生
关键词:  机器学习  性能预测  结构表征  计算模拟    
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 The traditional “trial and error” experimental methods are no longer applicable to the research of modern materials owing to the disadvantages of high cost, long period and great contingency.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
               出版日期:  2021-08-10      发布日期:  2021-08-31
ZTFLH:  TP181  
基金资助: 国家重点研发计划项目(2016YFB0301100);重庆市自然科学基金(cstc2017jcyjBX0040);国家自然科学基金(51531002)
作者简介:  米晓希,博士研究生,2018年毕业于重庆大学获得硕士学位。现在为重庆大学博士研究生,在汤爱涛教授的指导下进行研究,主要从事基于机器学习的镁合金组织与性能的研究。
汤爱涛,博士,教授,博士研究生导师,国家镁合金材料工程技术研究中心骨干研究人员。以镁合金、铝合金和复合材料为重点,主要从事材料数据库、材料的计算模拟以及高性能材料的研究。1984年本科毕业于重庆大学冶金系,2004年博士毕业于重庆大学材料学院,先后担任了五门本科课程和一门研究生课程的教学工作,是“计算机在材料科学与工程中的应用课程”的骨干教师。获得多项国家授权发明专利,发表重要论文60多篇。
潘复生,中国工程院院士,西北工业大学工学博士,重庆大学材料学科教授,博士研究生导师。1977年参加工作,1993年加入九三学社,第十一届全国政协委员,国务院学位委员会学科评议组成员,九三学社中央委员。历任重庆大学讲师、副研究员、系副主任、研究所所长、研究生院副院长、轻金属研究院院长等职。曾留学英国牛津大学、日本千叶大学和德国国家材料研究所。已在国内外重要刊物发表论文180余篇,出版著作7部(本)。
引用本文:    
米晓希, 汤爱涛, 朱雨晨, 康靓, 潘复生. 机器学习技术在材料科学领域中的应用进展[J]. 材料导报, 2021, 35(15): 15115-15124.
MI Xiaoxi, TANG Aitao, ZHU Yuchen, KANG Jing, PAN Fusheng. Research Progress of Machine Learning in Material Science. Materials Reports, 2021, 35(15): 15115-15124.
链接本文:  
http://www.mater-rep.com/CN/10.11896/cldb.20060168  或          http://www.mater-rep.com/CN/Y2021/V35/I15/15115
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