Machine learning in materials genome initiative:A review

• 作者: Yingli Liu Chen Niu Zhuo Wang Yong Gan Yan Zhu Shuhong Sun Tao Shen
• 来源期刊: 材料科学技术（英文版）
• 年，卷（期）: 2020,（22）
• 页码范围: 113-122
• 页数: 10页
• 语种: 英文

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 dis-covery 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 pre-diction,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.