Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data created by modern experimental and computational techniques is becoming more readily available, data-driven or machine learning methods have opened new paradigms for the discovery and rational design of materials. In this issue, Zhou and his colleagues introduced various machine learning methods and related software or tools. Main ideas and basic procedures for employing these approaches in materials research were highlighted. Recent representative applications of machine learning for functional material design were discussed.
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