Active Learning Guided Discovery of High Entropy Oxides Featuring High H2-productionClick to copy article linkArticle link copied!
- Siyang NieSiyang NieEngineering Research Center of Advanced Rare Earth Materials, Department of Chemistry, Tsinghua University, Beijing 100084, ChinaMore by Siyang Nie
- Yan XiangYan XiangSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Biomedical Engineering, Duke University, Durham, North Carolina 27705, United StatesMore by Yan Xiang
- Liang Wu*Liang Wu*Email: [email protected]School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaMore by Liang Wu
- Guang Lin*Guang Lin*Email: [email protected]Department of Mathematics and School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, United StatesMore by Guang Lin
- Qingda Liu*Qingda Liu*Email: [email protected]Engineering Research Center of Advanced Rare Earth Materials, Department of Chemistry, Tsinghua University, Beijing 100084, ChinaMore by Qingda Liu
- Shengqi ChuShengqi ChuBeijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, ChinaMore by Shengqi Chu
- Xun Wang*Xun Wang*Email: [email protected]Engineering Research Center of Advanced Rare Earth Materials, Department of Chemistry, Tsinghua University, Beijing 100084, ChinaMore by Xun Wang
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 gcat–1 min–1) during the water–gas shift reaction, surpassing benchmarks set by established catalysts such as Pt/γ–Al2O3 (135 μmol gcat–1 min–1) and Cu/ZnO/Al2O3 (81 μmol gcat–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.
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