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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 un-fortunately 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 materi-als 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)applica-tion.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 qual-ity 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.