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

Researchers at Tohoku University have developed a data-driven model that efficiently predicts the dehydrogenation barriers of magnesium hydride (MgH₂), a promising solid-state hydrogen storage material. This model uses the crystal Hamilton population orbital and atomic hydrogen distances to derive a distance-energy ratio, making predictions faster and less computationally intensive than traditional methods. Validated against experimental data, the model shows excellent agreement, advancing the development of high-performance hydrogen storage solutions and potentially applicable to other metal hydrides.

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