Missions

The researcher will work on an innovative project that combines Ab Initio Molecular Dynamics (AIMD) simulations with artificial intelligence approaches to investigate gas-surface interactions in atmospheric and astrophysical contexts.
The objective is to develop a computational framework that integrates molecular dynamics with potential energy surfaces generated by machine learning models (MLPs). This framework aims to improve both the accuracy and efficiency of simulations, enabling a detailed understanding of reactivity, energy transfer, and dissipation mechanisms at gas-solid interfaces.
The work will involve the generation of ab initio data, the training of AI models to build high-dimensional potential energy surfaces (PES), the implementation of automated strategies for selecting relevant configurations, and the execution of large-scale molecular dynamics simulations on model systems relevant to atmospheric and interstellar chemistry.

Activités

Development of codes in Python and Fortran
Use of software combining electronic structure calculations and molecular dynamics simulations (e.g., VASP)
Development and application of artificial intelligence methods based on neural networks for the modeling of potential energy surfaces (MLPs)

Compétences

Experience in Fortran and Python programming
Experience in the use of artificial intelligence methods
Experience in DFT calculations
Experience in molecular dynamics calculations

Contexte de travail

The position will be localize at PhLAM laboratory of Université de Lille in the PCMT team :
https://phlam.univ-lille.fr
https://phlam.univ-lille.fr/recherche/pcmt

Contraintes et risques

Computing work