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Machine-Learning-Accelerated Development of Efficient Mixed Protonic-Electronic Conducting Oxides as the Air
Ning Wang1,2, Baoyin Yuan3, Chunmei Tang2
1Huangpu Hydrogen Energy Innovation Centre, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, 510006, P. R. China.
Advanced Materials (Deerfield Beach, Fla.)
|September 30, 2022
Summary
Machine learning accelerates the discovery of novel mixed protonic-electronic conducting oxides for high-performance protonic ceramic cells (PCCs). This approach identifies efficient air electrode materials, improving PCC electrochemical performance.
Area of Science:
- Materials Science
- Electrochemistry
- Computational Chemistry
Background:
- Protonic ceramic cells (PCCs) require efficient mixed protonic-electronic conducting oxides as air electrodes.
- Current development relies on time-consuming, costly trial-and-error methods.
Purpose of the Study:
- To accelerate the discovery of novel mixed protonic-electronic conducting oxides using machine learning.
- To establish guidelines for designing high-performance air electrode materials for PCCs.
Main Methods:
- Machine learning model trained on published literature data.
- Prediction of hydrated proton concentration (HPC) for 3200 candidate oxides.
- Evaluation of feature importance for HPC prediction.
Main Results:
- Identified (La0.7 Ca0.3 )(Co0.8 Ni0.2 )O3 (LCCN7382) as a promising air electrode material.
- Experimental validation confirmed predicted HPC values for LCCN7382.
- PCCs utilizing LCCN7382 demonstrated satisfactory performance in electrolysis and fuel cell modes.
Conclusions:
- Machine learning significantly accelerates the discovery of mixed protonic-electronic conducting oxides.
- LCCN7382 shows potential as an efficient air electrode for PCCs.
- This study presents a new ML-driven pathway for materials development in energy devices.
