Designing hexagonal close packed high entropy alloys using machine learning

Abstract High entropy alloys (HEAs) have drawn significant interest in the materials research community owing to their remarkable physical and mechanical properties. These improved physicochemical properties manifest due to the formation of simple solid solution phases with unique microstructures. Though several pathbreaking HEAs have been reported, the field of alloy design, which has the potential to guide alloy screening, is still an open topic hindering the development of new HEA compositions, particularly ones with hexagonal close packed (hcp) crystal structure. In this work, an attempt has been made to develop an intelligent extra tree (ET) classification model based on the key thermodynamic and structural properties, to predict the phase evolution in HEAs. The results of correlation analysis suggest that all the selected thermodynamic and structural features are viable candidates for the descriptor dataset. Testing accuracy of above 90% along with excellent performance matrices for the ET classifier reveal the robustness of the model. The model can be employed to design novel hcp HEAs and as a valuable tool in the alloy design of HEAs in the future.