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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2502.08017 |
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| _version_ | 1866913686709338112 |
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| author | Tuchinda, Nutth Schuh, Christopher A. |
| author_facet | Tuchinda, Nutth Schuh, Christopher A. |
| contents | Modeling solute segregation to grain boundaries at near first-principles accuracy is a daunting task, particularly at finite concentrations and temperatures that require accurate assessments of solute-solute interactions and excess vibrational entropy of segregation that are computationally intensive. Here, we apply a generalized machine learning potential for 16 elements, including Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W and Zr, to provide a self-consistent spectral database for all of these energetic components in of 240 binary alloy polycrystals. The segregation spectra of Al-based alloys are validated against past quantum-accurate simulations and show improved predictive ability with some existing atom probe tomography experimental data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grain Boundary Segregation Spectra from a Generalized Machine-learning Potential Tuchinda, Nutth Schuh, Christopher A. Materials Science Modeling solute segregation to grain boundaries at near first-principles accuracy is a daunting task, particularly at finite concentrations and temperatures that require accurate assessments of solute-solute interactions and excess vibrational entropy of segregation that are computationally intensive. Here, we apply a generalized machine learning potential for 16 elements, including Ag, Al, Au, Cr, Cu, Mg, Mo, Ni, Pb, Pd, Pt, Ta, Ti, V, W and Zr, to provide a self-consistent spectral database for all of these energetic components in of 240 binary alloy polycrystals. The segregation spectra of Al-based alloys are validated against past quantum-accurate simulations and show improved predictive ability with some existing atom probe tomography experimental data. |
| title | Grain Boundary Segregation Spectra from a Generalized Machine-learning Potential |
| topic | Materials Science |
| url | https://arxiv.org/abs/2502.08017 |