Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization.

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness () and high compressive fracture strain (). Initially, we constructed data sets containing 172,467 data with 161 features for and , respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the and prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the of three candidates have shown significant improvements compared to the samples with similar in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2-14.8 at %), Nb (4-25 at %), and Mo (3-9.9 at %) in order to design HEAs with high hardness and ductility.