Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models

Materials informatics employs machine learning (ML) models to map the relationship between a targeted property and various materials descriptors, providing new avenues to accelerate the discovery of new materials. However, the possible ML models and materials descriptors are numerous, and a rational recipe to rapidly choose the best combination of the two is needed. In the present study, we propose a systematic framework that utilizes a genetic algorithm (GA) to efficiently select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs). The optimized classification model allows an accuracy for identifying solid-solution and non-solid-solution HEAs to be up to 88.7% and further for recognizing body-centered-cubic (BCC), face-centered-cubic (FCC), and dual-phase HEAs to reach 91.3%. Furthermore, by employing an active learning approach, several HEAs with largest classification uncertainties were selected, experimentally synthesized and phase-identified, and augmented to the initial dataset to iteratively improve the ML model. The method serves as a general algorithm to select materials descriptors and ML models for various material problems including classification and optimization of targeted properties.