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Machine-learning interatomic potential for radiation damage and defects in tungsten
- Source :
- Phys. Rev. B 100, 144105 (2019)
- Publication Year :
- 2019
-
Abstract
- We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been long-standing deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.
- Subjects :
- Physics - Computational Physics
Condensed Matter - Materials Science
Subjects
Details
- Database :
- arXiv
- Journal :
- Phys. Rev. B 100, 144105 (2019)
- Publication Type :
- Report
- Accession number :
- edsarx.1908.07330
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1103/PhysRevB.100.144105