Multi-scale Modeling of Materials
- m3 -
HEAD
We develop innovative and predictive models to understand the properties of matter, fill gaps in experiments, guide and control atomic-scale technological processes, and enhance performance and innovation.
From atom to process
Our team develops and applies predictive atomistic models for technological processes and biology. We rely on a multi-scale approach, implementing a broad spectrum of expertise, including quantum calculations, kinetic Monte Carlo, and the development of original tools.
Simulation of processes and formation of interface layers
The main objective of these activities is to understand the formation of interface layers in growth processes. We explore the relationships between material composition and structure, and the technological parameters of the processes used.
Atomic modeling for long-term reliability of electronic devices
We study atomic-scale defects to ensure long-term performance of devices. Utilizing simulations and numerical models, we characterize the impacts of defects on semiconductor materials, paving the way for strategic advancements in electronic design.
Head
Scientific executive
Postdoctoral
PhD
Intern
Partnership guest
Latest publications
2024
Journal articles
2023
Journal articles
2022
Journal articles
2021
Journal articles
Conference papers
2020
Journal articles
Conference papers
2019
Journal articles
Book sections
Conference papers
European Projects
- Horizon-MSCA-MAMBA (2023-2027)
- H2020-ICT-MUNDFAB (2019-2023)
ANR Projects
- PRCE "GeSPAD" (2020-2024)
Others
- IQOcc PhD grant (2023-2026)
- pARTn: an implementation of the Activation Relaxation Technique plugin to explore the surface of potential energies
Publication : pARTn: a plugin implementation of the Activation Relaxation Technique nouveau that takes over the FIRE minimization algorithm - Matic Poberznik, Miha Gunde, Nicolas Salles, Antoine Jay, Anne Hémeryck, Nicolas Richard, Normand Mousseau, Layla Martin-Samos - Computer Physics Communication 295 (2024), 108961, doi: https://doi.org/10.1016/j.cpc.2023.108961
Gitlab : https://gitlab.com/mammasmias/artn-plugin.
THESIS / HDR
2023
2022
2021
2020
2019
2016
Jobs / Interships
REJOINDRE
Notre équipe de recherche
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