Digital hydrogen platform (DigHyd): a rigorously curated database for hydrogen storage materials empowered by AI-assisted literature mining

Abstract

Solid-state hydrogen storage materials are promising candidates for safe and compact hydrogen storage; however, data-driven discovery in this field remains limited by the availability of large-scale, well-curated datasets. Here, we present the Digital Hydrogen Platform (DigHyd: www.dighyd.org ), a rigorously curated database comprising > 4,000 experimental literature sources and > 30,000 data entries on hydrogen storage materials, constructed through AI-assisted literature mining combined with human-in-the-loop validation. In addition to gravimetric hydrogen storage density (w), DigHyd also covers thermodynamic parameters, specifically the enthalpy (∆H) and entropy (∆S) changes associated with hydrogenation reactions, primarily defined as $$\:M+\frac{1}{2}{\text{H}}_{2}\rightleftharpoons\:M\text{H}$$ . These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van’t Hoff analysis. By focusing on ∆H and ∆S rather than fixing equilibrium pressure at a single temperature, DigHyd enables flexible evaluation of equilibrium behavior under application-specific operating conditions. Statistical analyses reveal distinct distributions of thermodynamic parameters across material classes, together with broad compositional variability within representative hydride systems. As a representative application, we performed composition-based symbolic-regression modeling using a curated single-phase or near-single-phase subset, which achieved predictive performance comparable to state-of-the-art black-box models while providing compact, physically interpretable relationships for w, $$\:{P}_{\text{e}\text{q},\text{R}\text{T}}$$ (equilibrium pressure at room temperature), ∆H, or ∆S. The resulting descriptor map identifies recurring physical factors, including host atomic mass, lattice geometry, elastic stiffness, metal filling factor, and electronegativity-derived descriptor correlations, which rationalize the trade-off between w and $$\:{P}_{\text{e}\text{q},\text{R}\text{T}}$$ . Overall, DigHyd provides a rigorously curated thermodynamic dataset that serves as a reliable basis for data-driven analyses of hydrogen storage materials and supports systematic exploration of structure–property relationships.