Data mining of stable, low-cost metal oxides as potential electrocatalysts

Abstract

Metal oxides (MOs) are a class of electrocatalysts which could be the low-cost alternatives to precious metals. However, many MOs suffer from poor stability under electrochemical operating conditions. The Materials Project stands out as one of the largest computational materials databases to date, where the bulk Pourbaix diagrams are essential in assessing the aqueous stability of potential electrocatalysts. Herein, we performed data mining from the Materials Project database to identify potentially stable MOs for industrially important electrocatalytic reactions including oxygen reduction reaction (ORR), oxygen evolution reaction (OER), chlorine evolution reaction (CER), hydrogen evolution reaction (HER), and nitrogen reduction reaction (NRR). We found that many MOs can be potentially stable under electrocatalytic conditions, especially in neutral and alkaline medium. Finally, we summarized those MOs that had been previously experimentally synthesized but haven’t been explored as electrocatalysts. This comprehensive assessment effectively narrows down the exploration scope and facilitates the evaluation of material stability.

Keywords

• Data mining
• Electrocatalysts
• Metal Oxides
• Stability
• Materials Project