Phase Prediction Methodologies for Rapid Screening of High Entropy Alloys

Abstract Increasing gas turbine hot section temperatures is a venue to improve fuel efficiency, thus pushing the need for new, higher temperature resistant materials. Experimental results show that high entropy alloys (HEAs), consisting of near equiatomic amounts of various metallic elements with no clear solvent, exhibit excellent high temperature mechanical properties, corrosion resistance, and good strength to weight ratios. This has resulted in significantly increased interest in studying HEAs for potential gas turbine application over the past decade. The properties of HEAs tend to be highly sensitive to phase composition; however, pre-existing phase prediction methodologies used for traditional alloy compositions, with a clearly defined solvent, are generally not well suited to HEAs. Additionally, the combination of the vast design space and complex elemental interactions render experimental exploration of new HEAs unfeasible at scale, necessitating higher throughput methods. Computer aided calculation of phase diagrams and first principal calculations are well established methods for predicting the phase structure of hypothetical alloy compositions, however they tend to be very computationally intensive and thus their speed is highly limited by available hardware. Two prediction methods capable of rapidly screening candidate composition are empirically developed design parameters based on values derived from the elemental composition of the alloys and machine learning models trained using available experimental HEA phase compositions. This simplifies the calculations and the input parameters required for both methodologies are readily available. This work compares the effectiveness of a variety of empirical design parameters and a pre-trained machine learning model, based on a convolutional neural network architecture, at predicting the resultant phases in various HEA compositions, including the W-Nb-Mo-Ta-Ti-Zr alloy system.