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Automated platforms for electrocatalyst discovery

Nature Reviews Clean Technology volume 1, page 826 (2025)Cite this article

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New advanced materials are needed to meet escalating global energy needs. Exploring high-dimensional material design spaces is a task beyond human capabilities, so integrating artificial intelligence (AI) with real-world experiments can facilitate practical material innovations. Active learning techniques, particularly Bayesian optimization with Gaussian processes, are highly effective for handling these complex optimization problems. Additionally, recent developments in large language models substantially enhance experimental design by synthesizing existing scientific literature. Combining these techniques with automated robotics allows for the potential replacement of labour-intensive and time-consuming manual approaches.

The system enables the rapid extraction of fundamental chemistry–structure–property relationships, accelerating materials innovation. In particular, it is suitable for electrochemical applications, including oxygen evolution reactions, hydrogen evolution reactions, CO2 reduction, formate oxidation and battery electrolyte optimization. It facilitates the exploration of reactions traditionally studied in standard electrochemical cells, with necessary adjustments for gas-tight or inert atmosphere conditions. Leveraging this system has accelerated the discovery of novel high-performance materials and enabled optimization of synthesis processes previously inaccessible by conventional experimental methods.

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Acknowledgements

The author thanks his PhD advisor J. Li for valuable guidance and discussions, and collaborator Z. Ren for the initial set-up of the platform.

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  1. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Zhen Zhang

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  1. Zhen Zhang
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Correspondence to Zhen Zhang.

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The author declares no competing interests.

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Zhang, Z. Automated platforms for electrocatalyst discovery. Nat. Rev. Clean Technol. 1, 826 (2025). https://doi.org/10.1038/s44359-025-00113-6

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  • DOI: https://doi.org/10.1038/s44359-025-00113-6

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