Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials
(2026) ACS Energy Letters — Vol. 11, n° 9, p. 6007-6025 (2026)
Files
nz-2026-018568.pdf
Open Access - Adobe PDF
- 10.43 MB
Details
- Authors
Jang, Seong-Hoon 
Yao, Yiwen Kutsukake, Kentaro - et. al.
- Abstract
- 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.
- Affiliations
Citations
Jang, S.-H., Yao, Y., Liu, C., Zhang, L., Zhang, D., Jia, X., Tran, H. B., Cheng, E. J., Sato, R., Ohashi, Y., Sato, T., Hashimoto, Y., Allendorf, M. D., Artrith, N., Baricco, M., Borgschulte, A., Broom, D. P., Cao, A., Chen, B. W. J., et al. (2026). Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials. ACS Energy Letters, 11(9), 6007-6025. https://doi.org/10.1021/acsenergylett.6c01856 (Original work published 2026)