Work

Machine learning and non-thermal external fields: A dual-track paradigm for engineering high-performance of Mg-based hydrogen storage materials

Year: 2026

Type: article

Abstract: • A dual-track paradigm for Mg-based hydrogen storage materials was proposed. • Machine learning can accelerate the design and screening of novel materials. • Non-thermal external fields enable efficient synthesis and superior properties. Confronted with the inherent thermodynamic and kinetic challenges of Mg-based hydrogen storage materials (HSMs), research strategies have evolved from traditional alloying, nano-structuring, and catalytic modification toward a new dual-track paradigm that integrates machine learning (ML)-based rational design with non-thermal external field (NTEF)-assisted precision regulation. ML significantly accelerates the design and screening of novel HSMs through efficient performance prediction, key descriptor extraction, and microscopic mechanism elucidation, while NTEF provides a powerful experimental means for non-equilibrium synthesis, microstructure optimization, and de-/hydrogenation behavior control by leveraging its unique high energy efficiency and tunability. These two approaches complement each other and jointly advance Mg-based HSMs toward higher performance, lower cost, and superior cycling stability, thereby establishing a solid scientific and technological foundation for safe, efficient hydrogen storage and transportation as well as large-scale hydrogen-thermal coupled application.

Source: Journal of Magnesium and Alloys

DOI: 10.1016/j.jma.2026.101998

Authors: Panpan Zhou, Lixin Chen

Institutions: Hohai University, Zhejiang University

Language: English

Topic: Hydrogen Storage and Materials

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,500