中国有色金属学报(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 33 No. 7 July 2023 |
（1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
2. Materials Genome Institute, Shanghai University, Shanghai 200444, China;
3. Zhejiang Laboratory, Hangzhou 311100, China;
4. Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki 305-8573, Japan）
Abstract:Exploring the rules of high entropy alloys (HEAs) phase formation has clear guiding significance for the design of new alloys. An end-to-end framework was proposed to select the feature subset and machine learning (ML) model from the feature pool and model pool, respectively. In this framework, each model in the pool is to determine its materials feature subset based on the feature importance. The final model was confirmed through the evaluation of the fitting result of every model and its feature subset. This method extracts important factors affecting the phase formation of HEAs. The results show that the chosen model could classify 430 HEAs into five phases, with test accuracy of 87.8%. And the model analysis suggests that the formation of single-phase solid solution is often inhibited when the atomic size difference is greater than 8.295%.
Key words: feature selection; high entropy alloys; machine learning; phase prediction; Hume-Rothery rules
