Abstract
High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years, due to their promising engineering application. Previous experimental and computational studies of HEAs focused mainly on equiatomic or near equiatomic HEAs. However, there is probably far more treasure in those non-equiatomic HEAs with carefully designed composition. In this study, the molecular dynamics (MD) simulation combined with machine learning (ML) methods was used to predict the mechanical properties of non-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensile test of 900 HEA single-crystal samples by MD simulation. Eight ML models were investigated and compared for the binary classification learning tasks, ranging from shallow models to deep models. It was found that the kernel-based extreme learning machine (KELM) model outperformed others for the prediction of yield stress and Young’s modulus. The accuracy of the KELM model was further verified by the large-sized polycrystal HEA samples. The results show that computational simulation combined with ML methods is an efficient way to predict the mechanical performance of HEAs, which provides new ideas for accelerating the development of novel alloy materials for engineering applications.
Highlights
The concept of “high-entropy alloys” (HEAs), in which metals are mixed by five or more elements in equiatomic or near-equiatomic proportions, was proposed by Yeh and co-workers [1], and independently by Cantor and co-workers [2] in 2004
molecular dynamics (MD) simulation combined with machine learning (ML) methods was used to investigate the mechanical properties of non-equiatomic CuFeNiCrCo High-entropy alloys (HEAs) samples
This study provides a new method for the design and prediction of HEA properties, and provides useful guidance for sample preparation in the experimental stage
Summary
The concept of “high-entropy alloys” (HEAs), in which metals are mixed by five or more elements in equiatomic or near-equiatomic proportions, was proposed by Yeh and co-workers [1], and independently by Cantor and co-workers [2] in 2004. If only 38 transition metals were considered, the number of new alloys would still be as high as about 500,000 [5]. Due to their considerable structural and functional potential, as well as the richness of design, different combinations of the elements and their compositions need to be explored to understand the potentials of HEAs further. Our understanding of these new alloys is still very limited. As suggested by Yeh, the pioneer of HEAs, ‘there is probably far more treasure in those nonequimolar alloys with carefully designed composition and tailored microstructures’ [11]
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