most citedDigital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

Physics-Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Yuhang Wang, Qian Wang, Seong-Hoon Jang +1

Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and incons…

cond-mat.mtrl-sci2026

Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

Seong-Hoon Jang, Yiwen Yao, Chuanyu Liu +66

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evo…

cond-mat.mtrl-sci2026

A unified descriptor framework for hydrogen storage capacity and equilibrium pressure in interstitial hydrides

Seong-Hoon Jang, Di Zhang, Xue Jia +9

Hydrogen is a promising energy carrier, yet its practical deployment is limited by the lack of storage materials that simultaneously achieve high storage capacity () and practic…

cond-mat.mtrl-sci20262 cited

Digital Hydrogen Platform (DigHyd): A Rigorously Curated Database for Hydrogen Storage Materials Empowered by AI-Assisted Literature Mining

Seong-Hoon Jang, Di Zhang, Xue Jia +8

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 availabil…

cond-mat.mtrl-sci2026

Competing Hydrogenation Pathways to Metastable CaH Revealed by Machine-Learning-Potential Molecular Dynamics

Ryuhei Sato, Peter I. C. Cooke, Maélie Caussé +8

The synthesis of the high- superhydride CaH has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mecha…