Data Mining Reveals 68 Potential New Electrocatalysts – Enerzine

A team of researchers explored the possibility of using data mining to accelerate the identification of low-cost metal oxide electrocatalysts, a step that could accelerate the global transition to renewable energy.

Research for sustainable energy

The world’s dependence on fossil fuels has led scientists to explore renewable energy sources.

Electrochemical conversion technologies such as fuel cell propulsion, water electrolysis and metal-air batteries offer promising strategies for the transition to a sustainable energy future. On the other hand, the dependence on precious metals in many electrocatalytic reactions presents economic and ecological challenges.

Metal oxides have the potential to be game-changing due to their stability and lower cost than noble metals, especially under alkaline electrocatalytic conditions. However, searching for these metal oxides is resource-intensive as scientists rely on a trial-and-error process.

Use data as a solution

“Data mining is a viable solution to this problem, so we decided to investigate the opportunities and challenges of this strategy to search for metal oxides,” he says Hao LiAssociate Professor at the Advanced Institute for Materials Research (WPI-AIMR), Tohoku University and corresponding author of the article.

To this end, Hao Li and his colleagues used the data available in the Materials Project database. Identification of 68 promising stable metal oxide electrocatalysts under certain circumstances.

Workflow of the data mining process for identifying water-stable metal oxides (MOs). a) Flowchart of the data mining process for identifying stable MOs from the Materials Project database. b) Number of MOs after each selection step and c) stable bulk MOs sorted by the number of metallic elements. d) Number of OMs that are stable in aqueous medium under different conditions. Photo credit: Hao Li et al.

Promising results, but adjustments needed

The researchers found that the database promoted Sb2WO6 as an acid-stable metal oxide. However, the researchers found that this contradicted later experimental observations under alkaline ORR conditions.

Additional post-catalysis characterizations, electrochemical surface state analysis, and pH-coupled microkinetic modeling revealed that the Sb2WO6 surface undergoes electrochemical passivation under ORR potentials, forming an active and stable surface for the 4-electron oxygen (ORR) reduction reaction.

The study results suggest that data mining, while promising, requires further refinement for widespread adoption. “A refined strategy needs to be developed that takes into account electrochemical and activity-induced surface stability,” emphasized Hao Li.


The researchers hope to explore more electrocatalysts for oxygen evolution reaction and hydrogen evolution reaction in the future by combining data mining, surface state analysis and activity analysis.

For better understanding

What is data mining?

Data mining involves analyzing large amounts of data to discover patterns and trends hidden within the data.

What is an electrocatalyst?

An electrocatalyst is a substance that accelerates electrochemical reactions.

What is the Oxygen Reduction Reaction (ORR)?

The oxygen reduction reaction is a chemical reaction that occurs in fuel cells and metal-air batteries in which oxygen (O2) is reduced to produce water (H2O).

What is the oxygen evolution reaction (OER)?

The oxygen evolution reaction is a chemical reaction that occurs in electrolyzers in which water (H2O) is oxidized to produce oxygen (O2).

What is the hydrogen evolution reaction (HER)?

The hydrogen evolution reaction is a chemical reaction that occurs in electrolyzers in which water (H2O) is reduced to produce hydrogen (H2).


Article: “Identification of stable electrocatalysts initialized by data mining: Sb2WO6 for oxygen reduction” – DOI: 10.1002/advs.202305630

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