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Data-driven model rapidly predicts dehydrogenation barriers in solid-state materials

Researchers from China, Japan and the US have developed a data-driven model to predict the dehydrogenation barriers of magnesium hydride (MgH 2), a promising material for solid-state hydrogen storage. This advancement holds significant potential for enhancing hydrogen storage technologies, a crucial component in the transition to sustainable energy solutions. An open-access paper on the work appears in the journal 

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