1.School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
2.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Perovskite materials have become one of the hotspots in modern materials science research due to their complex chemical compositions, diverse crystal structures and rich physical properties. In this paper, by combining the modeldriven approach and the data-driven approach, a materials intelligent computing framework integrating feature engineering and active learning is constructed to improve the model accuracy and system performance. Through the collaborative optimization of data layout and dynamic scheduling, a sure independence screening and sparsifying operator (SISSO) parallel computing method for material features is proposed to alleviate the problems of low accuracy and high computational cost faced by the SISSO algorithm when establishing the feature engineering model and reduce the impact of data quality on the model. An active learning method oriented to material data is constructed to deal with the complexity of material data labeling and eliminate noisy data.