Molecular dynamics simulation and machine learning of mechanical response in non-equiatomic FeCrNiCoMn high-entropy alloy

High-entropy alloys (HEAs) have attracted a wide range of academic interest for their promising properties as structural materials, among which the equiatomic FeCrNiCoMn HEAs have been reported to possess a series of superior properties. However, one may have to change the alloy composition from the equiatomic composition to improve a specific material property. In this study, molecular dynamics simulation combined with machine learning methods was used to study the mechanical properties of non-equiatomic FeCrNiCoMn HEAs. A database describing the relationship between materials composition and mechanical properties was established based on a tensile test of 300 HEA single-crystal samples by MD simulation. We investigated and compared three ML models for the learning task of yield stress, including support vector machine (SVM), kernel-based extreme learning machine (KELM), and deep neural network (DNN). It was found that the DNN model outperformed others for the binary classification of yield stress. The elemental composition strategy was used to guide the design of polycrystal FeCrNiCoMn samples, and the accuracy of the DNN model was further verified by the polycrystal samples. We show in this contribution that computational study combined with machine learning method can provide instructive guidance for the design of high-strength HEA and accelerate the development of new alloy materials.