Data-Efficient Design of High-Entropy Oxygen Carriers for Chemical Looping Using Active Learning

High-entropy materials, first demonstrated in metallic alloys and later extended to oxides and other systems, unlock a vast compositional space with properties suited for catalysis, energy, and structural materials. However, the high compositional complexity makes systematic exploration challenging, and only a small portion of the design space has been studied. To address this, we introduce an active learning strategy that integrates predictive modeling, uncertainty estimation, and iterative sampling to efficiently navigate embedded compositional material spaces. This approach continuously learns from previous evaluations, focusing subsequent searches on the most promising regions while reducing both time and data requirements. We demonstrate this methodology in the search for high-entropy oxygen carriers for chemical looping, where it rapidly accelerates discovery and identifies promising candidates more effectively than conventional trial-and-error or grid-search approaches. Importantly, this strategy is general and well-suited to exploring the vast space of multicomponent materials.