Missions

This one-year postdoctoral project aims to use multi scale atomic simulation methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting molecules, but the microscopic mechanisms governing their adsorption strength, monolayer formation, temperature stability, and response to corrosive ions remain insufficiently understood. The project will combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic environments that include solvent effects, thermal fluctuations, and the ingress of species such as chloride ions.

Activities

First, DFT calculations will be used to characterize adsorption geometries, energetics, and surface-chemistry trends across selected metals and their oxides. These data will support the construction of a machine-learning force field tailored to NHC–surface systems, enabling large-scale molecular dynamics simulations of self-assembled monolayers (SAMs). The ML-FF will allow us to explore monolayer formation, packing, defects, and degradation mechanisms under operational conditions. A key component of the project is the systematic screening of NHC SAM formation as a function of wingtip substituents, assessing how steric and electronic variations influence adsorption strength, monolayer density, order, and overall corrosion-barrier performance. The final outcome will be a predictive framework and design guidelines for robust NHC-based protective coatings.

Skills

The candidate should hold a PhD in computational chemistry, materials science, or a closely related field, with strong experience in DFT calculations (preferably periodic systems) and familiarity with surface science. Experience with molecular dynamics simulations and at least basic knowledge of machine-learning approaches for atomistic modeling are highly desirable. Skills in Python and experience with scientific computing environments (Linux, HPC clusters) are wellcome. Background knowledge in adsorption phenomena, catalysis, or corrosion science is a plus, along with good communication skills and the ability to work in an interdisciplinary environment.

Work Context

This postdoctoral position, funded by the French National Research Agency (ANR – UniLAP), is part of a project that combines both experimental and theoretical approaches. The research activities will take place primarily within the Surface Physical Chemistry team at the Institut de Recherche de Chimie Paris. The researcher will work in close collaboration with the project partners (L. Fensterbank, Collège de France; F. Ribot, Sorbonne Université; and B. Laïk, UPEC)