Building a physics-aware AI ecosystem for solid-state hydrogen storage materials

TL;DR

This paper presents a unified AI framework that integrates physics-based modeling, experimental feedback, and data infrastructure to accelerate the discovery of solid-state hydrogen storage materials.

Contribution

It introduces a novel closed-loop, physics-aware AI ecosystem that enhances materials discovery through adaptive, physically consistent optimization and digital twin integration.

Findings

Enables autonomous, physically consistent optimization of hydrogen storage materials

Integrates experimental feedback with AI-driven inverse design

Establishes a pathway for digital-twin-enabled materials discovery

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 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.