Abstract: Data-driven method utilizes machine learning(ML) to mine hidden rules in data, conforming to the "fourth paradigm". A great deal of basic data is needed for this method. By comparing the domestic and aboard materials basic data platforms and analyzing researches based on these platforms, there are two problems: lack of data and lack of unified acquisition standard. In view of this, the data acquisition standard in line with materials genome initiative(MGI) is introduced. And the framework and sources of data platform specially for iron and steel wear-resistant materials are given. The factors affecting the properties of iron and steel wear-resistant materials are analyzed, and the characteristics of various feature selection techniques are discussed. Then several ML algorithms, applied in material science researches successfully, are reviewed. The application scenarios of each algorithm are analyzed, the relative merits of them are discussed, and their performances are compared. Finally, some suggestions are summarized to provide guidance on how to choose feature selection methods and ML algorithms. It is pointed out that the data-driven method has a good application prospect in property prediction, new material discovery and automatic experiment.

Key words: data-driven, machine learning algorithm, feature engineering, basic data platform, iron and steel wear-resistant materials