TY - JOUR
T1 - Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials
AU - 69 authors, including
AU - Jang, Seong-Hoon
AU - Yao, Yiwen
AU - Liu, Chuanyu
AU - Zhang, Linda
AU - Zhang, Di
AU - Jia, Xue
AU - Tran, Hung Ba
AU - Cheng, Eric Jianfeng
AU - Sato, Ryuhei
AU - Ohashi, Yusuke
AU - Sato, Toyoto
AU - Hashimoto, Yusuke
AU - Allendorf, Mark D.
AU - Artrith, Nongnuch
AU - Baricco, Marcello
AU - Borgschulte, Andreas
AU - Broom, Darren P.
AU - Cao, Ang
AU - Chen, Benjamin Wei Jie
AU - Chen, Lixin
AU - Chen, Ping
AU - Cho, Eun Seon
AU - Deledda, Stefano
AU - Ding, Zhao
AU - Dornheim, Martin
AU - Felderhoff, Michael
AU - Filinchuk, Yaroslav
AU - Froudakis, George E.
AU - Gao, Mingxia
AU - Gennett, Thomas
AU - Guo, Zaiping
AU - Hamada, Ikutaro
AU - Hattrick-Simpers, Jason
AU - Hauback, Bjorn C.
AU - Hirscher, Michael
AU - Jensen, Torben R.
AU - Jia, Baohua
AU - Kim, Hyoung Seop
AU - Kondo, Takahiro
AU - Kutsukake, Kentaro
AU - Li, Xiao-Yan
AU - Liu, Tongliang
AU - Ma, Piao
AU - Mao, Jianfeng
AU - Oh, Hyunchul
AU - Paskevicius, Mark
AU - Pickard, Chris J.
AU - Pundt, Astrid
AU - Ramirez-Cuesta, Anibal J.
AU - Saitoh, Hiroyuki
AU - Ou, Pengfei
AU - Peng, Jiayu
AU - Orimo, Shin-ichi
AU - Li, Hao
PY - 2026/8/14
Y1 - 2026/8/14
N2 - 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. © 2026 The Authors. Published by American Chemical Society.
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. © 2026 The Authors. Published by American Chemical Society.
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001850507200001
U2 - 10.1021/acsenergylett.6c01856
DO - 10.1021/acsenergylett.6c01856
M3 - RGC 21 - Publication in refereed journal
SN - 2380-8195
JO - ACS Energy Letters
JF - ACS Energy Letters
ER -
SDG 7 Affordable and Clean Energy