Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
Abstract The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius dif...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55759-w |
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| author | Xi Zhang Sergiy V. Divinski Blazej Grabowski |
| author_facet | Xi Zhang Sergiy V. Divinski Blazej Grabowski |
| author_sort | Xi Zhang |
| collection | DOAJ |
| description | Abstract The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius diffusion behavior has remained elusive, largely due to the lack of effective computational tools. Here we propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at the density-functional-theory level. With the aid of a bespoke machine-learning interatomic potential, the temperature-dependent vacancy formation and migration Gibbs energies of the prototype system body-centered cubic (BCC) tungsten are shown to be strongly affected by anharmonicity. This finding explains the physical origin of the experimentally observed non-Arrhenius behavior of tungsten self-diffusion. A remarkable agreement between the calculated and experimental temperature-dependent self-diffusivity and, in particular, its curvature is revealed. The proposed computational framework is robust and broadly applicable, as evidenced by first tests for a hexagonal close-packed (HCP) multicomponent high-entropy alloy. The successful applications underscore the attainability of an accurate ab initio diffusion database. |
| format | Article |
| id | doaj-art-a3e0a5f2019741279f9234925c0dc7fe |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-a3e0a5f2019741279f9234925c0dc7fe2025-01-05T12:41:11ZengNature PortfolioNature Communications2041-17232025-01-0116111110.1038/s41467-024-55759-wAb initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungstenXi Zhang0Sergiy V. Divinski1Blazej Grabowski2Institute for Materials Science, University of StuttgartInstitute of Materials Physics, University of MünsterInstitute for Materials Science, University of StuttgartAbstract The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius diffusion behavior has remained elusive, largely due to the lack of effective computational tools. Here we propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at the density-functional-theory level. With the aid of a bespoke machine-learning interatomic potential, the temperature-dependent vacancy formation and migration Gibbs energies of the prototype system body-centered cubic (BCC) tungsten are shown to be strongly affected by anharmonicity. This finding explains the physical origin of the experimentally observed non-Arrhenius behavior of tungsten self-diffusion. A remarkable agreement between the calculated and experimental temperature-dependent self-diffusivity and, in particular, its curvature is revealed. The proposed computational framework is robust and broadly applicable, as evidenced by first tests for a hexagonal close-packed (HCP) multicomponent high-entropy alloy. The successful applications underscore the attainability of an accurate ab initio diffusion database.https://doi.org/10.1038/s41467-024-55759-w |
| spellingShingle | Xi Zhang Sergiy V. Divinski Blazej Grabowski Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten Nature Communications |
| title | Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten |
| title_full | Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten |
| title_fullStr | Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten |
| title_full_unstemmed | Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten |
| title_short | Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten |
| title_sort | ab initio machine learning unveils strong anharmonicity in non arrhenius self diffusion of tungsten |
| url | https://doi.org/10.1038/s41467-024-55759-w |
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