5 papers · 1 filter
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…
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…
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…
Competing Hydrogenation Pathways to Metastable CaH Revealed by Machine-Learning-Potential Molecular Dynamics
Ryuhei Sato, Peter I. C. Cooke, Maélie Caussé +7
The synthesis of the high- superhydride CaH has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mecha…
Predicting room-temperature conductivity of Na-ion super ionic conductors with the minimal number of easily-accessible descriptors
Seong-Hoon Jang, Randy Jalem, Yoshitaka Tateyama
Given the vast compositional possibilities NaSiPAsO, Na-ion superionic conductors (NASICON) are attractive but complicate for designing materi…