Fig. 1: Overview and architecture of MLMD.

a Data module, which encompasses material databases, data visualization, and feature engineering. b Regression module, which comprises a group of ML regression algorithms. These algorithms can be further utilized in the surrogate optimization module. c Classification module, which involves a group of ML classification algorithms. These algorithms can be further leveraged in the surrogate optimization module. d Surrogate optimization module, where the ML model is incorporated into numerical algorithms to accelerate materials design. e Active learning module, sampling methods based on Bayesian are provided to search the material composition space and discover novel materials, particularly under limited available data. f Other module, which provides advanced ML algorithms such as transfer learning, dimensionality reduction, and interpretable ML.