机器学习在新材料筛选方面的应用进展

Recent Advance of Machine Learning in Selecting New Materials

新材料产业是许多相关领域技术变革的基础,也是新能源、航空航天、电子信息等高新技术产业发展的先导.传统研发手段由于成本高、效率低、商业化周期长等不利因素无法满足现代社会的发展需求.近年来大数据与人工智能不断深入结合,以数据驱动为核心的机器学习在新材料设计、筛选以及性能预测等方面取得巨大进展,极大促进了新材料的研发与应用.本综述总结了机器学习的基本过程及其在材料科学中常用的算法和相关材料数据库,重点介绍了机器学习在不同功能上的应用以及在催化剂材料、锂离子电池、半导体材料和合金材料等领域的性能预测和材料开发中的最新进展,并对其下一步在新材料应用方面提出展望.

The new material industry is the foundation of technological change in many related fields,and also the forerunner of the development of new energy,aerospace,electronic information and other high-tech industries.Traditional means cannot meet the development needs of modern society because of disadvantages such as high cost,low efficiency and long commercial cycle.In recent years,with the application of big data combined with artificial intelligence in a deeper degree,data-driven machine learning has made great progress in the design,screening and performance prediction of new materials,which has greatly promoted the development and application of new materials.In this review,the basic process of machine learning,the algorithms commonly used in materials science and the relevant materials database are summarized.This review focuses on the application of machine learning in different functions,as well as the performance prediction in the fields of catalyst materials,lithium-ion batteries,semiconductor materials and alloy materials,presenting the latest progress in materials development.Finally,machine learning in the application of new materials are analyzed and prospected.