物理感知AI可加速氢存储材料发现

September 8, 2026 Physics-aware AI could accelerate hydrogen storage materials discovery by Tohoku University edited by Swati Mestri, reviewed by Robert Egan Swati Mestri Scientific Editor Meet our editorial team Behind our editorial process Robert Egan Senior Editor Meet our editorial team Behind our editorial process Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked peer-reviewed publication trusted source proofread The GIST Add as preferred source Overview of the proposed physics-aware ecosystem for hydrogen-storage materials discovery. Databases, physics-grounded models, AI-driven design, automated experiments and a digital twin form a cycle in which predictions and experimental results continually improve one another. Credit: Seong-Hoon Jang et al. Hydrogen could help store renewable energy and power fuel cells, but storing it remains difficult because hydrogen gas has a low density under everyday conditions. Compressing it to high pressure or cooling it into a liquid requires substantial energy and specialized equipment. Solid materials that absorb or adsorb hydrogen offer an alternative, but finding materials that store enough hydrogen, release it under useful conditions, work quickly and survive repeated use remains a complex challenge. "Reliable AI-guided discovery in this field cannot come from faster predictions alone," said Seong-Hoon Jang, associate professor at Tohoku University. "It requires physical constraints, transparent data and continuous feedback from real experiments built into the process from the start." Artificial intelligence can search vast numbers of candidate materials, but a fast prediction is not necessarily a reliable one. Hydrogen-storage data are scattered across computational databases and scientific papers, and important details about sample preparation, measurement conditions, uncertainty and unsuccessful experiments are often missing. Models trained on incomplete records can recommend materials that are physically unrealistic, difficult to synthesize or unsuitable under practical operating conditions. Closed-loop discovery framework. (a) Prediction, synthesis, measurement and model refinement are repeated; (b) intelligent planning selects the next candidate while accounting for uncertainty; and (c) a digital twin synchronizes computational and experimental information. Credit: Seong-Hoon Jang et al. An international team led by Tohoku University has outlined a roadmap for making AI-guided hydrogen-storage research more trustworthy and useful. Drawing on discussions among 69 researchers spanning hydrogen-storage materials, AI, computational science and self-driving laboratories, the team proposes a physics-aware ecosystem connecting four elements: reproducibility-aware data, models constrained by thermodynamics and kinetics, AI-driven inverse design and experimental validation. In this framework, physical consistency, uncertainty, data provenance and experimental feedback are built into every stage, rather than checked only after an AI has proposed a candidate. The perspective paper is published in the journal ACS Energy Letters. The framework turns materials discovery into a learning cycle. AI proposes candidates, automated systems synthesize and measure them, and the results are returned to the database and model to inform the next decision. The authors also describe a longer-term concept they call a "digital twin," a virtual representation that stays synchronized with real experiments. This could help researchers detect model errors or material degradation and choose the next experiment where it will reduce uncertainty most effectively. The team highlights the Digital Hydrogen Platform (DigHyd), which organizes more than 30,000 thermodynamic entries from over 4,000 publications, as an example of the data foundation this approach requires. This work is a perspective rather than a report on a newly discovered storage material. It identifies the system-level changes needed to move the field from trial and error toward research that is reproducible, adaptive and able to learn continuously. Experimental validation and expert judgment remain essential, particularly because AI-generated candidates may not be synthesizable. "The limiting factors have not been a lack of computing power," Jang added. "They have been fragmented data, incomplete experimental context, weak physical consistency and poor feedback between prediction and experiment. Addressing these could make the search for safer, more compact and more practical hydrogen storage faster and more reliable, supporting future clean-energy systems." Next steps for the project include standardizing how hydrogen-storage data and experimental conditions are recorded, expanding information on kinetics, cycling and degradation, and connecting physics-grounded models with AI design tools. Tohoku University is also integrating these tools with automated synthesis and measurement systems and is developing autonomous experimental workflows under the JST GteX program (JPMJGX23H1). Publication details Seong-Hoon Jang et al, Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials, ACS Energy Letters (2026). DOI: 10.1021/acsenergylett.6c01856 Journal information: ACS Energy Letters Key concepts Green hydrogen systemsHydrogen energy membranesAI-enabled digital twinsMachine learning methodologies Provided by Tohoku University Who's behind this story? Swati Mestri Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile → Robert Egan Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile → Citation: Physics-aware AI could accelerate hydrogen storage materials discovery (2026, September 8) retrieved 8 September 2026 from https://techxplore.com/news/2026-09-physics-aware-ai-hydrogen-storage.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. 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