Machine learning in materials genome initiative: A review. (15th November 2020)

• Record Type: Journal Article
• Authors: Liu, Yingli; Niu, Chen; Wang, Zhuo; Gan, Yong; Zhu, Yan; Sun, Shuhong; Shen, Tao
• Is Part Of: Journal of materials science & technology. Volume 57(2020)
• Page Start: 113
• Page End: 122
• Publication Date: 2020-11-15
• Subjects: Materials genome initiative (MGI) -- Materials database -- Machine learning -- Materials properties prediction -- Materials design and discovery; Metals -- Periodicals; Materials science -- Periodicals; Materials science; Metals; Periodicals; 620.1105
• DOI: 10.1016/j.jmst.2020.01.067
• Languages: English
• ISSNs: 1005-0302

Abstract

Abstract: 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.