Defects and Impurity Properties of VN precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917218386706432 |
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| author | Stroud, R. S. Reynolds, C. Melichar, T. Haley, J. Carter, M. Moody, M. Hardie, C. Bowden, D. Nguyen-Manh, D. Wenman, M. R. |
| author_facet | Stroud, R. S. Reynolds, C. Melichar, T. Haley, J. Carter, M. Moody, M. Hardie, C. Bowden, D. Nguyen-Manh, D. Wenman, M. R. |
| contents | VN precipitates used to strengthen ARAFM steels for fusion applications dissolve under high Fe ion irradiation (100 dpa at 10^-3 dpa s^-1, 600 C). This study examined point defects and solute substitutions using atom probe tomography, machine learning interatomic potentials, and density functional theory. Combined with transmission electron microscopy, results show N-vacancies and substitutional Cr exist in VN precipitates before irradiation. Monte Carlo simulations and collision cascade simulations confirm ordered vacancies at operating temperatures help mitigate irradiation damage. However, solute additions disrupt vacancy ordering and enhance irradiation-induced damage, potentially accelerating dissolution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19720 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Defects and Impurity Properties of VN precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation Stroud, R. S. Reynolds, C. Melichar, T. Haley, J. Carter, M. Moody, M. Hardie, C. Bowden, D. Nguyen-Manh, D. Wenman, M. R. Materials Science Applied Physics Computational Physics VN precipitates used to strengthen ARAFM steels for fusion applications dissolve under high Fe ion irradiation (100 dpa at 10^-3 dpa s^-1, 600 C). This study examined point defects and solute substitutions using atom probe tomography, machine learning interatomic potentials, and density functional theory. Combined with transmission electron microscopy, results show N-vacancies and substitutional Cr exist in VN precipitates before irradiation. Monte Carlo simulations and collision cascade simulations confirm ordered vacancies at operating temperatures help mitigate irradiation damage. However, solute additions disrupt vacancy ordering and enhance irradiation-induced damage, potentially accelerating dissolution. |
| title | Defects and Impurity Properties of VN precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation |
| topic | Materials Science Applied Physics Computational Physics |
| url | https://arxiv.org/abs/2503.19720 |