Phase prediction in high-entropy alloys through uncertainty sampling and symbolic classification-based parameter discovery (opens in new tab)

Data imbalance represents critical challenges for data-driven approaches in materials data modeling. In the present work, active learning models were developed to mitigate data imbalance in phase selection of high-entropy alloys (HEAs). Three alloy phase classifiers were established based on three datasets containing 149 features: one classifier evaluated the glass-forming ability of alloys; another distinguished among SS, SS + IM, and IM; and the third differentiated BCC, FCC, BCC + FCC, and...