High‐throughput calculation integrated with stacking ensemble machine learning for predicting elastic properties of refractory multi‐principal element alloys - Jin, Chengchen; Xiong, Kai; Luo, Congtao; Fang, Hui; Pu, Chaoguang; Dai, Hua; Zhang, Aimin; Zhang, Shunmeng; Wang, Yingwu (Materials Genome Engineering Advances, 2025)
The traditional trial‐and‐error method for designing refractory multi‐principal element alloys (RMPEAs) is inefficient due to a vast compositional design space and high experimental costs. To surmount this challenge, the data‐driven material design based on machine learning (ML) has emerged as a critical tool for accelerating materials design. However, the absence of robust datasets impedes the exploitation of machine learning in designing novel RMPEAs. High‐throughput (HTP) calculations have enabled the creation of such datasets. This study addresses these challenges by developing a data‐driven framework for predicting the elastic properties of RMPEAs, integrating HTP calculations with ML. A big dataset of RMPEAs including 4536 compositions was constructed using the new proposed HTP method. A novel stacking ensemble regression algorithm combining multilayer perceptron (MLP) and gradient boosting decision tree (GBDT) was developed, which achieved 92.9% accuracy in predicting the elastic properties of Ti‐V‐Nb‐Ta alloys. Verification experiments confirmed the ML model's accuracy and robustness. This integration of HTP calculations and ML provides a cost‐effective, efficient, and precise alloy design strategy, advancing RMPEAs development. This study effectively integrated high‐throughput calculations and machine learning to accurately predict the elastic properties of Ti‐V‐Nb‐Ta refractory multi‐principal element alloys (RMPEAs), facilitating a more efficient approach to alloy design.