Structure features play an important role in machine learning models for the materials investigation.Here,two topology-based features for the representation of material structure,specifically structure graph and algeb...Structure features play an important role in machine learning models for the materials investigation.Here,two topology-based features for the representation of material structure,specifically structure graph and algebraic topology,are introduced.We present the fundamental mathematical concepts underlying these techniques and how they encode material properties.Furthermore,we discuss the practical applications and enhancements of these features made in specific material predicting tasks.This review may provide suggestions on the selection of suitable structural features and inspire creativity in developing robust descriptors for diverse applications.展开更多
基金support from the Guangdong Basic and Applied Basic Research Foundation(2020A1515110843),Young S&T Talent Training Program of Guangdong Provincial Association for S&T(SKXRC202211)Chemistry and Chemical Engineering Guangdong Laboratory(1922018)+3 种基金Soft Science Research Project of Guangdong Province(2017B030301013)National Natural Science Foundation of China(22109003)Natural Science Foundation of Shenzhen(JCYJ20190813110605381)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen.
文摘Structure features play an important role in machine learning models for the materials investigation.Here,two topology-based features for the representation of material structure,specifically structure graph and algebraic topology,are introduced.We present the fundamental mathematical concepts underlying these techniques and how they encode material properties.Furthermore,we discuss the practical applications and enhancements of these features made in specific material predicting tasks.This review may provide suggestions on the selection of suitable structural features and inspire creativity in developing robust descriptors for diverse applications.
