Molecular Dynamics simulations of Al-Ti metallic alloy melts using a transferable machine-learning potential
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914516185382912 |
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| author | Kato, Yuna Brillo, Jürgen Holland-Moritz, Dirk Yang, Fan Hansen, Thomas C. Voigtmann, Thomas Heitmeier, Linnea |
| author_facet | Kato, Yuna Brillo, Jürgen Holland-Moritz, Dirk Yang, Fan Hansen, Thomas C. Voigtmann, Thomas Heitmeier, Linnea |
| contents | We investigate the structural and dynamical properties of binary aluminum-titanium liquid metallic alloys, as a function of temperature and composition. We make use of MD-simulations, using a transferable machine-learning potential developed by Song et al. [Nature Communications 15, 10208 (2024)], and compare our results to experimental data. Although this potential was initially trained on solid properties, we find good agreement between the experimental data and the simulation results for the liquid state. The excess volume and compositional changes of the structure are captured well by the machine-learned potential. The simulation allows to disentangle local packing from chemical-ordering effects; the latter are found to be weak in Al-Ti. Dynamical quantities like the viscosity and the diffusion coefficients are also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26362 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Molecular Dynamics simulations of Al-Ti metallic alloy melts using a transferable machine-learning potential Kato, Yuna Brillo, Jürgen Holland-Moritz, Dirk Yang, Fan Hansen, Thomas C. Voigtmann, Thomas Heitmeier, Linnea Materials Science Soft Condensed Matter We investigate the structural and dynamical properties of binary aluminum-titanium liquid metallic alloys, as a function of temperature and composition. We make use of MD-simulations, using a transferable machine-learning potential developed by Song et al. [Nature Communications 15, 10208 (2024)], and compare our results to experimental data. Although this potential was initially trained on solid properties, we find good agreement between the experimental data and the simulation results for the liquid state. The excess volume and compositional changes of the structure are captured well by the machine-learned potential. The simulation allows to disentangle local packing from chemical-ordering effects; the latter are found to be weak in Al-Ti. Dynamical quantities like the viscosity and the diffusion coefficients are also discussed. |
| title | Molecular Dynamics simulations of Al-Ti metallic alloy melts using a transferable machine-learning potential |
| topic | Materials Science Soft Condensed Matter |
| url | https://arxiv.org/abs/2604.26362 |