Missions

As part of the RAISIN project (Representativeness of Ion Irradiations to Simulate Neutron Irradiations) the primary damage in nickel and in FeNiCr alloys of different concentrations, models for austenitic steels, was modeled by molecular dynamics. This work made it possible to show, for example, that the composition has a significant effect on the number of monovacancies and the number of defects at low energies, while at higher energies, the effect of the chosen interaction potential takes precedence over these two descriptors. The analysis of the database thus constituted must be continued and is the subject of this postdoctoral contract, to obtain a more precise description of the clusters of defects. Indeed, the description we currently have only specifies the type of cluster (vacancy or interstitial) and its size. It would be useful to be able to know the distribution between the different types of 3D clusters (A15, stacking fault tetrahedra, etc.) and dislocation loops. For this, we could for example use the defect detection method developed by Goryaeva et al. [Goryaeva et al., Nature Communications (2020) 11:4691] based on an analysis of local atomic environments using “machine learning” methods (MiLaDy). In parallel with this analysis of the database, and to have a better idea of the properties of the potential used, it will be necessary to characterize more precisely the solid solutions with different compositions and the defects (formation and migration energy).

Activities

Main activities
In collaboration with Christophe Domain from EDF and Andrée de Backer, under contract at UMET, the candidate must:
- Carry out an in-depth bibliographic research on recent methods to detect defect clusters taking solutes into account.
- Implement this method to analyze the cascade database
- Study the properties of the different solid solutions modeled: presence or absence of short or long range order, phase stability, characteristics of basic defects: vacancies and self-interstitials (energy of formation and migration).
- Use statistical tools to study the impact of composition and potential on the population of defects created
Other activities-
- Present the results during the RAISIN project working group meetings
- Communicate scientific results in the form of communications at conferences and publications

Skills

Knowledge
- Have solid knowledge in materials science, thermodynamics, statistical physics, physical metallurgy and modeling at the atomic scale.
- Knowledge of statistical tools (PCA, kmeans, etc.) in R or python
- Knowledge of Machine Learning methods
- Mastery of scientific English
Know-how
-Know how to prioritize actions
- Have interpersonal and educational qualities
- Demonstrate rigor and autonomy; be organized
- Have a sense of teamwork
- Know how to report

Work Context

The mission is part of the RAISIN project led by the CNRS. The work will mainly take place within the UMET laboratory, located at the University of Lille (Cité Scientifique in Villeneuve d'Ascq) in collaboration with EDF and as part of the joint EM2VM laboratory.

The position is located in a sector falling under the protection of scientific and technical potential (PPST) and therefore requires, in accordance with regulations, that your arrival be authorized by the competent authority of the MESR. »

The position is located in a sector under the protection of scientific and technical potential (PPST), and therefore requires, in accordance with the regulations, that your arrival is authorized by the competent authority of the MESR.

Constraints and risks

NONE

Additional Information

NEEDS RAISIN Program