Optimizing high-entropy alloys using deep neural networks

Abstract

Exceptional mechanical properties of multi-principal element alloys have been typically achieved through manual trial-and-error approaches. The advances in artificial neural networks (ANN) have recently allowed for the development of predictive alloy design ANN models that take into account a large variety of prior experimental findings. However, when mechanical performance is considered, prior processing protocols (e.g. annealing, cold working) are almost impossible to track across different research groups and material classes. In this work, we propose to reverse the use of the yield stress point, setting it as an explicit input (instead of predictive output) parameter that is then used for the quantification of the material state, towards predicting material properties, such as the Ultimate Tensile Strength (UTS). This novel approach specifically addresses the challenge of predicting and improving ductility, a common issue in existing methods focused on yield stress, hardness, and phases. By combining available, validated databases of eleven elements and 734 total materials, we develop an ANN that receives as input the compositions out of 11 possible elements and the "observed" yield stress. Then, we utilize this model to perform predictions for 5-element concentrated alloys with exceptional UTS. We show that this ANN modeling strategy leads to exceptional confidence scores, and then, we use this ANN model for the design of HEAs, which we further discuss.