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High-entropy alloys (HEAs) with multiple constituent elements have beenextensively studied in the past 20 years due to their promising engineeringapplication. Previous experimental and computational studies of HEAs focusedmainly on equiatomic or near equiatomic HEAs. However, there is probably farmore treasure in those non-equiatomic HEAs with carefully designed composition.In this study, molecular dynamics (MD) simulation combined with machinelearning (ML) methods were used to predict the mechanical properties ofnon-equiatomic CuFeNiCrCo HEAs. A database was established based on a tensiletest of 900 HEA single-crystal samples by MD simulation. We investigated andcompared eight ML models for the learning tasks, ranging from shallow models todeep models. It was found that the kernel-based extreme learning machine (KELM)model outperformed others for the prediction of yield stress and Young'smodulus. The accuracy of the KELM model was further verified by the large-sizedpolycrystal HEA samples.

Prediction of mechanical properties of non-equiatomic high-entropy alloy by atomistic simulation and machine learning