Post-Doctorat - Machine Learning Based Md For Two Temperature Metals H/F CEA

Since a decade Machine Learning Interatomic Potential (MLIP) has proven unprecedented capacities in reproducing DFT reference data for very diverse configurations These potentials rely on defining atomic descriptor that encode the local geometrical environment and then learn the relation between the descriptor and the reference DFT data (energy or atomic forces). This function could be as simple as a linear function or as complex as a multilayer perceptron. As the interation potential between atoms depend on their electronic temperature we propose to learn and incorporate this dependance directly into the MLIP. Then, the Two-Temperature Model, where the diffusion equation for the electronic temperature is solved on a grid and the ionic motion is solved using MD will be employed to investigate out-of-equilibrium effects on the melting dynamics. In particular large scale MD will be used to simulate the melting of a full gold target (few tens of nm length) under laser absorption.
The first task for the candidate will be to locate or adapt existing machine learning potentials in the literature to accurately describe metals at high electronic temperatures. This potential will then be fitted on DFT-based simulations and used on simple systems. Then, large-scale molecular dynamics simulations will be conducted on HPC using the molecular dynamics code ExaSTAMP to investigate the time history of a metallic foil illuminated by a femtosecond laser. Throughout the post-doctoral position, the candidate will work in close collaboration with experimental physicists.
Conformément aux engagements pris par le CEA en faveur de l'intégration des personnes handicapées, cet emploi est ouvert à toutes et à tous. Le CEA propose des aménagements et/ou des possibilités d'organisation pour l'inclusion des travailleurs handicapés.
Participant à la protection nationale, une enquête administrative est réalisée pour tous les collaborateurs du CEA afin d'assurer l'intégrité et la sécurité de la nation.