Discovery of high-entropy perovskite oxygen carriers for chemical looping applications via an autonomous active learning protocol

Artikel i vetenskaplig tidskrift, 2026

The discovery and design of new materials are paramount for advancing green technologies. High-entropy oxides represent one such group that has been only tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Here, oxygen carriers for chemical looping processes have been identified using active learning-based strategies and first-principles-informed calculations. The proposed approaches were validated using an established computational framework for identifying high-entropy perovskites suitable for chemical looping air separation and dry reforming. The central insight gained was the identification of effective strategies, including greedy and Thompson-based sampling, informed by uncertainty estimates from Gaussian processes. Building on this knowledge, the concept was applied to the challenge of discovering high-entropy oxygen carriers for chemical-looping oxygen uncoupling. This resulted in both qualitative and quantitative outcomes, including lists of materials with high oxygen transfer capacities and configurational entropies. The top candidates were based on the known oxygen carrier CaMnO3 and included expected elements such as titanium, cobalt, and copper, as well as unexpected ones such as yttrium and samarium. The results suggest that adopting active learning approaches is critical for materials discovery, as these methods are already reshaping research practice and will soon become the norm.