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The lack of machine-readable experimental data impedes data-driven discoveries in catalysis research. To advance the FAIR principles — guidelines to improve the Findability, Accessibility, Interoperability and Reuse of digital assets — we have developed a catalysis plugin (called the Catalysis App) for the NOMAD platform that supports standardized data upload and features integrated visualization. This infrastructure provides a robust foundation for machine-learning workflows and the direct comparison of experimental data with theory.
Electrocatalysts enable the efficient interconversion of electrical and chemical energy for the sustainable production of fuels and chemicals. Here we highlight the importance of developing electrochemical adsorption isotherms to demystify complex reaction mechanisms and rationalize catalytic activity.
