Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys
Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang
Abstract
The development of refractory high-entropy alloys (RHEAs) through conventional trial-and-error approaches I s highly inefficient given the vast compositional space. To address this difficulty, a data-driven machine learning (ML) model was established to predict the compressive yield strength ( σ 0.2 ) of RHEAs, which provides a design strategy with two pivotal descriptors concerning lattice distortion and thermodynamic parameters. The LightGBM algorithm was utilized to train the model based on a RHEA yield strength dataset. The optimal descriptor set consists of T , G mix , D.B , VEC , μ , δr , and the resultant model obtained a determination coefficient ( R 2 ) of 0.88 and a mean absolute error ( MAE ) of 0.162 GPa in 10-fold cross validation, exhibiting remarkable accuracy and generalization. Interpretation methods clarified the influence of each descriptor. As a result, a linear equation was revealed as a decision boundary for the rapid design of RHEAs with high σ 0.2 . The effects of incorporating Ni and Al into the TiMoNbHf, TiTaNbHf, and TiZrNbHf matrix RHEAs on σ 0.2 were investigated. Experiments verified the application of such a model and the strategy with a mean error of 7.2%, and the designed TiMoNbHf(AlNi) 0.2 displayed σ 0.2 up to 1.61 GPa with fracture strain of 20%.