Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials

@article{jang2026building,
  title = {{Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials}},
  author = {Seong‐Hoon Jang and Yiwen Yao and Chuanyu Liu and Linda Zhang and Di Zhang and Xue Jia and Hung Ba Tran and Eric Jianfeng Cheng and Ryuhei Sato and Yusuke Ohashi and Toyoto Sato and Yusuke Hashimoto and Mark D. Allendorf and Nongnuch Artrith and Marcello Baricco and Andreas Borgschulte and Darren P. Broom and Ang Cao and Benjamin W. J. Chen and Lixin Chen and Ping Chen and Eun Seon Cho and Stefano Deledda and Zhu Ding and Martin Dornheim and Michael Felderhoff and Yaroslav E. Filinchuk and George E. Froudakis and Mingxia Gao and Thomas Gennett and Zaiping Guo and Ikutaro Hamada and Jason R. Hattrick-Simpers and Bjørn C. Hauback and Michael Hirscher and Torben R. Jensen and Baohua Jia and Hyoung Seop Kim and Takahiro Kondo and Kentaro Kutsukake and Xiao-Yan Li and Tongliang Liu and Piao Ma and Jianfeng Mao and Rana Mohtadi and Hyunchul Oh and Mark Paskevicius and Chris J. Pickard and Astrid Pundt and Anibal Javier Ramirez-Cuesta and Hiroyuki Saitoh and Kaihang Shi and Aloysius Soon and Chenghua Sun and Chris M. Wolverton and Hiroshi Yabu and Weijie Yang and Zhenpeng Yao and Xuebin Yu and Jianxin Zou},
  journal = {ACS Energy Letters},
  year = {2026},
  month = aug,
  doi = {10.1021/acsenergylett.6c01856},
  url = {https://doi.org/10.1021/acsenergylett.6c01856},
  note = {CC BY},
}

TY  - JOUR
TI  - Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials
AU  - Seong‐Hoon Jang
AU  - Yiwen Yao
AU  - Chuanyu Liu
AU  - Linda Zhang
AU  - Di Zhang
AU  - Xue Jia
AU  - Hung Ba Tran
AU  - Eric Jianfeng Cheng
AU  - Ryuhei Sato
AU  - Yusuke Ohashi
AU  - Toyoto Sato
AU  - Yusuke Hashimoto
AU  - Mark D. Allendorf
AU  - Nongnuch Artrith
AU  - Marcello Baricco
AU  - Andreas Borgschulte
AU  - Darren P. Broom
AU  - Ang Cao
AU  - Benjamin W. J. Chen
AU  - Lixin Chen
AU  - Ping Chen
AU  - Eun Seon Cho
AU  - Stefano Deledda
AU  - Zhu Ding
AU  - Martin Dornheim
AU  - Michael Felderhoff
AU  - Yaroslav E. Filinchuk
AU  - George E. Froudakis
AU  - Mingxia Gao
AU  - Thomas Gennett
AU  - Zaiping Guo
AU  - Ikutaro Hamada
AU  - Jason R. Hattrick-Simpers
AU  - Bjørn C. Hauback
AU  - Michael Hirscher
AU  - Torben R. Jensen
AU  - Baohua Jia
AU  - Hyoung Seop Kim
AU  - Takahiro Kondo
AU  - Kentaro Kutsukake
AU  - Xiao-Yan Li
AU  - Tongliang Liu
AU  - Piao Ma
AU  - Jianfeng Mao
AU  - Rana Mohtadi
AU  - Hyunchul Oh
AU  - Mark Paskevicius
AU  - Chris J. Pickard
AU  - Astrid Pundt
AU  - Anibal Javier Ramirez-Cuesta
AU  - Hiroyuki Saitoh
AU  - Kaihang Shi
AU  - Aloysius Soon
AU  - Chenghua Sun
AU  - Chris M. Wolverton
AU  - Hiroshi Yabu
AU  - Weijie Yang
AU  - Zhenpeng Yao
AU  - Xuebin Yu
AU  - Jianxin Zou
PY  - 2026
DA  - 2026/08/14
JO  - ACS Energy Letters
DO  - 10.1021/acsenergylett.6c01856
UR  - https://doi.org/10.1021/acsenergylett.6c01856
AB  - Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By constraining optimization with thermodynamics, kinetics, uncertainty, provenance, and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.
SN  - 2380-8195
ER  -