Data in support of "Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications using a Coupled Machine Learning and CALPHAD Methodology"

Abstract: This study aimed to utilise a combined Machine Learning (ML) and CALculations of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal forming applications that could serve as the basis for carbide reinforcement. The vast compositional space that High Entropy Alloys (HEAs) occupy, offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed revealed accuracies > 78.7% and mean absolute error of 66.1 HV for phase and hardness predictions. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, were experimentally fabricated and tested. The hardness predictions are found to be systematically under and over predicted depending on the alloy microstructure. In parallel, the phase classification models were found to lack sensitivity towards additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions showed promise for further experimental evaluation. These discrepancies were believed to be directly associated with the available databases but importantly have highlighted several avenues for both ML and database development.