Building a Physics-Aware AI Ecosystem for Solid-State Hydrogen Storage Materials
Создание физически информированной экосистемы искусственного интеллекта для материалов твердотельного хранения водорода
2026-08-14
SCID: 54.1/rnbrn5wa
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AI-driven inverse designclosed-loop materials discoverydigital twin-enabled discoveryphysics-aware AIsolid-state hydrogen storage materials
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Abstract (AI)
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.
Key Findings
1
Hydrogen storage materials discovery is limited by coupled multiscale thermodynamics, kinetics, microstructural evolution, fragmented data, and insufficient experimental integration.
2
Optimization is constrained by thermodynamics, kinetics, uncertainty, data provenance, and experimental feedback to promote physically consistent material design.
3
The framework enables adaptive optimization and establishes a pathway toward autonomous, digital-twin-enabled discovery of solid-state hydrogen storage materials.
4
The proposed framework unifies coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design in a closed-loop discovery paradigm.
Research Object
Solid-state hydrogen storage materials (HSMs)
Research Subject
Physics-consistent, AI-driven inverse design and closed-loop discovery of HSMs accounting for thermodynamics, kinetics, microstructural evolution, uncertainty, data provenance, and experimental feedback
Publication Details
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2026-08-14
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References available in scid.ai4
Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory2024
“DIVE” into hydrogen storage materials discovery with AI agents2026
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The Chemistry and Applications of Metal-Organic Frameworks2013