| Title: |
Point defect segregation at edge dislocations in a-Fe studied by kinetic activation-relaxation technique |
| Authors: |
Kvashin, Nikolai; Anento Moreno, Napoleón; Malerba, Lorenzo |
| Contributors: |
Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental |
| Publisher Information: |
Elsevier |
| Publication Year: |
2026 |
| Collection: |
Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| Subject Terms: |
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures; Point-defect dislocation interaction; Radiation effects; Adaptive kinetic Monte Carlo; Logistic regression algorithm |
| Description: |
The mechanical properties of crystalline materials such as metals, are strongly related to the mobility of dislocations, which is directly affected by their interaction with other defects present in the microstructure and acting as obstacles. Under irradiation conditions the number density of point defects increases substantially, leading to several phenomena at the atomic scale, some of which are related with the behaviour of dislocations as sinks for vacancies and self-interstitial atoms. In this work we present an in-depth study of the segregation process of point defects to an edge dislocation in a-Fe, performed with an on-the-fly kinetic Monte Carlo model, the kinetic activation-relaxation technique (k-ART). Our KMC simulations show that, in the vicinity of the dislocation core, the dynamics of vacancies and SIAs is accelerated before absorption. For the former, the preferential path is along the compression region while for the latter is along the tensile region. This work therefore provides a greater knowledge of the dynamic properties of point defects around of dislocations, such as free migration time, acceleration/deceleration of point defects motion and energies of absorption events. These results will allow more precise modelling of the microstructure evolution of polycrystalline materials, improving the predictive capabilities of existing models in the long term. In order to ensure transferability of these findings to other KMC models, the data obtained in the simulations have been used to train a prediction model based on a Machine Learning logistic regression algorithm. ; This work has been supported by the Euratom research and training programme 2019–2020 under grant agreement No 900018 (ENTENTE project). The scientific advice of Normand Mousseau and Laurent Béland regarding the application of the method for the present work is gratefully acknowledged. ; Peer Reviewed ; Postprint (published version) |
| Document Type: |
article in journal/newspaper |
| File Description: |
13 p.; application/pdf |
| Language: |
English |
| Relation: |
https://www.sciencedirect.com/science/article/pii/S0022311526000024; info:eu-repo/grantAgreement/EC/H2020/900018/EU/European Database for Multiscale Modelling of Radiation Damage/ENTENTE; https://hdl.handle.net/2117/450597 |
| DOI: |
10.1016/j.jnucmat.2026.156436 |
| Availability: |
https://hdl.handle.net/2117/450597; https://doi.org/10.1016/j.jnucmat.2026.156436 |
| Rights: |
http://creativecommons.org/licenses/by/4.0/ ; Open Access ; Attribution 4.0 International |
| Accession Number: |
edsbas.485EA287 |
| Database: |
BASE |