Machine-Learning-Accelerated Development of Efficient Mixed Protonic-Electronic Conducting Oxides as the Air Electrodes for Protonic Ceramic Cells.

Abstract

Currently, the development of high-performance protonic ceramic cells (PCCs) is limited by the scarcity of efficient mixed protonic-electronic conducting oxides that can act as air electrodes to satisfy the high protonic conductivity of electrolytes. Despite the extensive research efforts in the past decades, the development of mixed protonic-electronic conducting oxides still remains in a trial-and-error process, which is extremely time consuming and high cost. Herein, based on the data acquired from the published literature, the machine-learning (ML) method is introduced to accelerate the discovery of efficient mixed protonic-electronic conducting oxides. Accordingly, the hydrated proton concentration (HPC) of 3200 oxides is predicted to evaluate the proton conduction that is essential for enhancing the electrochemical performances of PCCs. Subsequently, feature importance for HPC is evaluated to establish a guideline for rapid and accurate design and development of high-efficiency mixed protonic-electronic conducting oxides. Thereafter, screened (La0.7 Ca0.3 )(Co0.8 Ni0.2 )O3 (LCCN7382) is prepared, and the experimental HPC adequately corresponds with the predicted results. Moreover, the PCC with LCCN7382 exhibits satisfactory electrochemical performances in electrolysis and fuel cell modes. In addition to the development of a promising air electrode for PCC, this study establishes a new avenue for ML-based development of mixed protonic-electronic conducting oxides.