Description

With the rapid development of quantum computing in recent years, the search for materials capable of storing and utilizing quantum bits (qubits) has intensified. The intrinsic properties of semiconductors make them promising candidates for such applications. Results from the ANR QUWIS project demonstrate that stable point defects can be created in semiconductor materials through heavy-ion bombardment. Depending on their stability, these defects could serve as excellent platforms for hosting qubits. However, existing models used to describe defects in oxides are insufficient to study and explain the stability of defects in semiconductors. Atomistic simulations of wide-bandgap semiconductors such as GaN, AlN, and SiC can provide insights into the unique characteristics of these materials and contribute to the development of theoretical models.

This PhD project aims to investigate the stability of defects at the atomic scale, focusing in particular on localized electronic clouds. Quantum methods such as Density Functional Theory (DFT) using the VASP or Quantum Espresso codes will be employed to study the electronic properties of these defects. Molecular dynamics simulations, capable of handling larger scales than ab initio calculations, will be used to model the effects of fast heavy-ion irradiation on impurity diffusion and recrystallization pathways leading to the formation of local defects. This will likely require the development of a potential that accounts for Coulomb interactions and the electronic environment of the defects. The project also aims at integrating Machine Learning tools to parameterize a classical model, enabling long-time simulations of recrystallization, defect diffusion, and the internal reorganization of materials.

 

Location:

Laboratoire CIMAP, site d’Alençon - Campus de Damigny, 61250 Damigny, France

Candidate profile:

Applicants should hold or be nearing completion of a Master's degree or an equivalent qualification in fields related to materials science, physics, or theoretical chemistry

Highly Sought Skills:

• Experience in programming (C++, Fortran, Python, etc.) or a strong interest in scientific computing and numerical methods.
• Knowledge of statistical physics, quantum chemistry, and/or atomistic modeling.
• Expertise in materials science.

Contact:

Germain CLAVIER : germain.clavier@unicaen.fr
Viwanou HOUNKPATI : viwanou.hounkpati@unicaen.fr
Jun CHEN : jun.chen@unicaen.fr

Références :
https://doi.org/10.1007/s10853-015-9069-y
https://doi.org/10.1016/j.nimb.2022.12.025

 

To apply, please send us:

• a cover letter
• a CV
• your M1 and M2 transcripts
• letters of recommendation (not mandatory, but recommended)

Skills required

Applicants should hold or be nearing completion of a Master's degree or an equivalent qualification in fields related to materials science, physics, or theoretical chemistry Highly Sought Skills: • Experience in programming (C++, Fortran, Python, etc.) or a strong interest in scientific computing and numerical methods. • Knowledge of statistical physics, quantum chemistry, and/or atomistic modeling. • Expertise in materials science.

Bibliography

https://doi.org/10.1007/s10853-015-9069-y https://doi.org/10.1016/j.nimb.2022.12.025

Keywords

Defects, Irradiation, Atomistic modelling, Machine Learning, Semiconductors