Title
Hydrogen diffusion in magnesium using machine learning potentials: a comparative study
Luca Leoni
Department of Physics and Astronomy ’Augusto Righi’, Alma Mater Studiorum - Universitá di Bologna
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Abstract
Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab-initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning. By considering different temperatures and concentration regimes, we obtain hydrogen diffusion coefficients and activation energy values which align remarkably well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force-field allows the retrieval of DFT-level accuracy at a fraction of the computational cost.
Keywords
Electronic properties and materialsMaterials for energy and catalysis
Object type
Language
English [eng]
Persistent identifier
Appeared in
Title
npj Computational Materials
Volume
11
ISSN
2057-3960
Issued
2025
Publication
Springer Science and Business Media LLC
Version type
Date issued
2025
Access rights
Rights
Rights statement
© The Author(s) 2025
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