Researchers Harness AI to Find Sustainable Methods for Hydrogen Fuel Production

Artificial Intelligence Researchers Harness AI to Find Sustainable Methods for Hydrogen Fuel Production

2024-03-04 Source: National Institute for Materials Science 2 min Reading Time

A Nims research team has developed an AI technique capable of expediting the identification of materials with desirable characteristics. Using this technique, the team was able to discover high-performance water electrolyzer electrode materials free of platinum-group elements.

The AI technique developed by the Nims team can predict material compositions with desired properties.(Source: AI generated/ Dall-E) — The AI technique developed by the Nims team can predict material compositions with desired properties.
(Source: AI generated/ Dall-E)

A team of researchers at the National Institute for Materials Science (Nims) has developed an artificial intelligence (AI) method that significantly accelerates the discovery of water electrolyzer electrode materials that do not require platinum-group elements, traditionally considered essential for the electrolysis of water. This breakthrough could potentially lower the costs of producing green hydrogen, a promising next-generation energy source aimed at achieving carbon neutrality.

The current production of green hydrogen relies heavily on water electrolyzers that use scarce and expensive platinum-group elements to catalyze the oxygen evolution reaction (OER), a key process in hydrogen production. The quest for cheaper, abundant alternatives has led to the exploration of new OER electrocatalysts made from common chemical elements. However, the sheer number of possible combinations has made this search a daunting task.

This research team developed an AI technique capable of accurately predicting the compositions of materials with desirable characteristics by switching prediction models depending on the sizes of the datasets available for analysis.(Source: Ken Sakaushi National Institute for Materials Science) — This research team developed an AI technique capable of accurately predicting the compositions of materials with desirable characteristics by switching prediction models depending on the sizes of the datasets available for analysis.
(Source: Ken Sakaushi National Institute for Materials Science)

The AI technique developed by the Nims team can predict material compositions with desired properties by adapting its prediction model based on the size of the dataset being analyzed. This approach enabled the researchers to sift through approximately 3000 potential materials in just a month — a task that would have otherwise taken nearly six years if done manually. The identified materials, composed of common metals such as manganese, iron, nickel, zinc, and silver, demonstrated superior electrochemical properties under certain conditions compared to the current leading material, ruthenium oxide.

The discovery not only showcases the potential of AI in enhancing material research but also marks a significant step towards sustainable hydrogen production. With silver being the least abundant of the identified metals but still 100 times more common than ruthenium, these new materials present a viable path to mass-producing green hydrogen through water electrolysis. The team plans to further utilize this AI technique in the development of new materials to improve the efficiency of electrochemical devices, contributing to global efforts towards carbon neutrality.

Original Article: Human-Machine Collaboration for Accelerated Discovery of Promising Oxygen Evolution Electrocatalysts with On-Demand Elements; ACS Central Science; DOI:10.1021/acscentsci.3c01009

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Date: 08.12.2025

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