摘要
Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
作者
宋志龙
陈曦雯
孟繁斌
程观剑
王陈
孙中体
尹万健
Zhilong Song;Xiwen Chen;Fanbin Meng;Guanjian Cheng;Chen Wang;Zhongti Sun;Wan-Jian Yin(College of Energy,Soochow Institute for Energy and Materials InnovationS(SIEMIS),and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,Soochow University,Suzhou 215006,China)
基金
Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)
the National Key Research and Development Program of China(Grant No.2016YFB0700700)
the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
China Post-doctoral Foundation(Grant No.7131705619).