Active Learning Guided Discovery of High Entropy Oxides Featuring High H2-production

Abstract

High entropy oxides (HEOs) represent a class of solid solutions comprising multiple elements, offering significant scientific potential. Due to the enormous combination types of elements, the design of HEOs with desirable properties within high-dimensional composition spaces has traditionally relied heavily on knowledge and intuition. In this study, we present an active learning (AL) strategy tailored to efficiently explore the vast compositional space of HEOs. Our approach operates as a closed-loop system, iteratively cycling through “Training, Prediction, and Experiment” stages. Across multiple AL iterations, we have successfully identified four novel HEOs from a vast array of potential compositions. These newly discovered materials exhibit exceptional stability and demonstrate outstanding performance in H₂ evolution rate (251 μmol g_cat^–1 min^–1) during the water–gas shift reaction, surpassing benchmarks set by established catalysts such as Pt/γ–Al₂O₃ (135 μmol g_cat^–1 min^–1) and Cu/ZnO/Al₂O₃ (81 μmol g_cat^–1 min^–1). X-ray photoelectron spectroscopy and density functional theory calculations revealed a loss of elemental identity in the selected HEOs. This catalyst discovery process underscores the efficacy of Machine Learning in accelerating the identification of HEOs with unique characteristics by effectively leveraging insights from limited experimental data.