Active-learning-guided discovery of alumina-forming refractory complex concentrated alloys

Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, but their compositional complexity challenges conventional discovery approaches. In this study, an active learning framework is introduced that integrates Gaussian process regression with Bayesian global optimization to accelerate the identification of oxidation-resistant RCCAs. Focusing on aluminum-containing quaternary systems, alloy and oxide descriptors were utilized to predict oxidation performance at 1000 ∘C. Beginning with a dataset of 81 experimentally validated alloys, the framework iteratively selected five-alloy batches, balancing exploration and exploitation to minimize experimental costs. After six iterations, two alloys (nominal Al30Mo5Ti15Cr50 and Al40Mo5Ti30Cr25) were identified that exhibited specific mass gains 0.12 HV0.5m3/kg) and predicted thermal expansion characteristics relevant to high-temperature coating applications, warranting further investigation. This work underscores the efficacy of active learning in traversing complex compositional landscapes to develop advanced materials for extreme environments.