948 citations · 1.6k across the 17 of their papers we have counts for
19 papers
When Literature Data Mislead Artificial Intelligence in Materials Discovery
Qian Wang, Ying Li, Ryuhei Sato +4
Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-de…
Why Ammoniated Lithium Borohydrides Liquefy and Resolidify?
Qian Wang, Zixin Xu, Ryuhei Sato +7
Ammonia (NH) absorption drives LiBHNH through a re-entrant ``solid--liquid--solid'' transition: LiBHNH is a well-defined solid ammoniate, compositio…
Breaking Bottlenecks in Solid Electrolyte Discovery with Large Artificial Intelligence Models
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng +22
Solid electrolytes (SEs) are central to next-generation metal batteries, yet their discovery remains constrained by fragmented data, limited transferability of simulations, and slo…
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…