Machine learning in materials genome initiative:A review
来源期刊：JOURNAL OF MATERIALS SCIENCE TECHNOLOG2020年第22期
论文作者：Yingli Liu Chen Niu Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen
摘 要：Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.
Yingli Liu1,2，Chen Niu1，Zhuo Wang3,4，Yong Gan5，Yan Zhu1，Shuhong Sun6，Tao Shen1,2
1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology2. Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology3. Light Alloy Research Institute, Central South University5. Chinese Academy of Engineering6. Faculty of Materials Science and Engineering, Kunming University of Science and Technology
摘 要：Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.
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