Predicting single-phase solid solutions in as-sputtered high entropy alloys: High-throughput screening with machine-learning model

Abstract

Searching for single-phase solid solutions (SPSSs) in high-entropy alloys (HEAs) is a prerequisite for the intentional design and manipulation of microstructures of alloys in vast composition space. However, to date, reported SPSS HEAs are still rare due to the lack of reliable guiding principles for the synthesis of new SPSS HEAs. Here, we demonstrate an ensemble machine-learning method capable of discovering SPSS HEAs by directly predicting quinary phase diagrams based only on atomic composition. A total of 2198 experimental structure data are extracted from as-sputtered quinary HEAs in the literature and used to train a random forest classifier (termed AS-RF) utilizing bagging, achieving a prediction accuracy of 94.6% compared with experimental results. The AS-RF model is then utilized to predict 224 quinary phase diagrams including ∼32,000 SPSS HEAs in Cr-Co-Fe-Ni-Mn-Cu-Al composition space. The extrapolation capability of the AS-RF model is then validated by performing first-principle calculations using density functional theory as a benchmark for the predicted phase transition of newly predicted HEAs. Finally, interpretation of the AS-RF model weighting of the input parameters also sheds light on the driving forces behind HEA formation in sputtered systems with the main contributors being: valance electron concentration, work function, atomic radius difference and elementary symmetries.