Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.14139 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911515958837248 |
|---|---|
| author | Jang, Seong-Hoon Zhang, Di Jia, Xue Tran, Hung Ba Zhang, Linda Sato, Ryuhei Hashimoto, Yusuke Sato, Toyoto Konno, Kiyoe Orimo, Shin-ichi Li, Hao |
| author_facet | Jang, Seong-Hoon Zhang, Di Jia, Xue Tran, Hung Ba Zhang, Linda Sato, Ryuhei Hashimoto, Yusuke Sato, Toyoto Konno, Kiyoe Orimo, Shin-ichi Li, Hao |
| contents | 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 ($ΔH$) and entropy ($ΔS$) changes associated with hydrogenation reactions, primarily defined as $M + \frac{1}{2} {\rm H}_2 \rightleftarrows M{\rm H}$. These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van't Hoff analysis. By focusing on $ΔH$ and $ΔS$ 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. Furthermore, both physically interpretable symbolic regression and black-box XGBoost models achieve comparable predictive performance for $w$ and equilibrium pressure at room temperature ($P_{\rm eq,RT}$), demonstrating internal consistency and learnable composition-property relationships within the curated dataset. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14139 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining Jang, Seong-Hoon Zhang, Di Jia, Xue Tran, Hung Ba Zhang, Linda Sato, Ryuhei Hashimoto, Yusuke Sato, Toyoto Konno, Kiyoe Orimo, Shin-ichi Li, Hao Materials Science 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 ($ΔH$) and entropy ($ΔS$) changes associated with hydrogenation reactions, primarily defined as $M + \frac{1}{2} {\rm H}_2 \rightleftarrows M{\rm H}$. These parameters were obtained by manually analyzing multi-temperature pressure-composition-temperature (PCT) data using van't Hoff analysis. By focusing on $ΔH$ and $ΔS$ 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. Furthermore, both physically interpretable symbolic regression and black-box XGBoost models achieve comparable predictive performance for $w$ and equilibrium pressure at room temperature ($P_{\rm eq,RT}$), demonstrating internal consistency and learnable composition-property relationships within the curated dataset. 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. |
| title | Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.14139 |