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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03081 |
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| _version_ | 1866916029872996352 |
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| author | Jang, Seong-Hoon Yao, Yiwen Liu, Chuanyu Zhang, Linda Zhang, Di Jia, Xue Tran, Hung Ba Cheng, Eric Jianfeng Sato, Ryuhei Ohashi, Yusuke Sato, Toyoto Hashimoto, Yusuke Allendorf, Mark Artrith, Nongnuch Baricco, Marcello Borgschulte, Andreas Broom, Darren P. Cao, Ang Chen, Benjamin W. J. Chen, Lixin Chen, Ping Cho, Eun Seon Deledda, Stefano Ding, Zhao Dornheim, Martin Felderhoff, Michael Filinchuk, Yaroslav Froudakis, George E. Gao, Mingxia Gennett, Thomas Guo, Zaiping Hamada, Ikutaro Hattrick-Simpers, Jason Hauback, Bjørn C. Hirscher, Michael Jensen, Torben R. Jia, Baohua Kim, Hyoung Seop Kondo, Takahiro Kutsukake, Kentaro Li, Xiao-Yan Liu, Tongliang Ma, Piao Mao, Jianfeng Mohtadi, Rana Oh, Hyunchul Paskevicius, Mark Pickard, Chris J. Pundt, Astrid Ramirez-Cuesta, Anibal Saitoh, Hiroyuki Shi, Kaihang Soon, Aloysius Sun, Chenghua Wolverton, Chris Yabu, Hiroshi Yang, Weijie Yao, Zhenpeng Yu, Xuebin Zou, Jianxin Hu, Shouyi Zhou, Panpan Lin, Xi Hu, Zhigang Zhou, Zhenhao Ou, Pengfei Peng, Jiayu Orimo, Shin-ichi Li, Hao |
| author_facet | Jang, Seong-Hoon Yao, Yiwen Liu, Chuanyu Zhang, Linda Zhang, Di Jia, Xue Tran, Hung Ba Cheng, Eric Jianfeng Sato, Ryuhei Ohashi, Yusuke Sato, Toyoto Hashimoto, Yusuke Allendorf, Mark Artrith, Nongnuch Baricco, Marcello Borgschulte, Andreas Broom, Darren P. Cao, Ang Chen, Benjamin W. J. Chen, Lixin Chen, Ping Cho, Eun Seon Deledda, Stefano Ding, Zhao Dornheim, Martin Felderhoff, Michael Filinchuk, Yaroslav Froudakis, George E. Gao, Mingxia Gennett, Thomas Guo, Zaiping Hamada, Ikutaro Hattrick-Simpers, Jason Hauback, Bjørn C. Hirscher, Michael Jensen, Torben R. Jia, Baohua Kim, Hyoung Seop Kondo, Takahiro Kutsukake, Kentaro Li, Xiao-Yan Liu, Tongliang Ma, Piao Mao, Jianfeng Mohtadi, Rana Oh, Hyunchul Paskevicius, Mark Pickard, Chris J. Pundt, Astrid Ramirez-Cuesta, Anibal Saitoh, Hiroyuki Shi, Kaihang Soon, Aloysius Sun, Chenghua Wolverton, Chris Yabu, Hiroshi Yang, Weijie Yao, Zhenpeng Yu, Xuebin Zou, Jianxin Hu, Shouyi Zhou, Panpan Lin, Xi Hu, Zhigang Zhou, Zhenhao Ou, Pengfei Peng, Jiayu Orimo, Shin-ichi Li, Hao |
| contents | 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 embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03081 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Building a physics-aware AI ecosystem for solid-state hydrogen storage materials Jang, Seong-Hoon Yao, Yiwen Liu, Chuanyu Zhang, Linda Zhang, Di Jia, Xue Tran, Hung Ba Cheng, Eric Jianfeng Sato, Ryuhei Ohashi, Yusuke Sato, Toyoto Hashimoto, Yusuke Allendorf, Mark Artrith, Nongnuch Baricco, Marcello Borgschulte, Andreas Broom, Darren P. Cao, Ang Chen, Benjamin W. J. Chen, Lixin Chen, Ping Cho, Eun Seon Deledda, Stefano Ding, Zhao Dornheim, Martin Felderhoff, Michael Filinchuk, Yaroslav Froudakis, George E. Gao, Mingxia Gennett, Thomas Guo, Zaiping Hamada, Ikutaro Hattrick-Simpers, Jason Hauback, Bjørn C. Hirscher, Michael Jensen, Torben R. Jia, Baohua Kim, Hyoung Seop Kondo, Takahiro Kutsukake, Kentaro Li, Xiao-Yan Liu, Tongliang Ma, Piao Mao, Jianfeng Mohtadi, Rana Oh, Hyunchul Paskevicius, Mark Pickard, Chris J. Pundt, Astrid Ramirez-Cuesta, Anibal Saitoh, Hiroyuki Shi, Kaihang Soon, Aloysius Sun, Chenghua Wolverton, Chris Yabu, Hiroshi Yang, Weijie Yao, Zhenpeng Yu, Xuebin Zou, Jianxin Hu, Shouyi Zhou, Panpan Lin, Xi Hu, Zhigang Zhou, Zhenhao Ou, Pengfei Peng, Jiayu Orimo, Shin-ichi Li, Hao Materials Science 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 embedding physical constraints and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs. |
| title | Building a physics-aware AI ecosystem for solid-state hydrogen storage materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.03081 |