Post-doctorat - Machine learning based MD for two temperature metals - H/F
il y a 2 semaines
Informations générales
Entité de rattachement
La Direction des Applications Militaires (DAM) du CEA, au cœur des enjeux de la dissuasion nucléaire Française, cherche ses futurs talents. Organisme inclusif, le CEA est handi-accueillant : nos emplois sont ouverts à toutes et tous. Associer les forces et les compétences de chacun pour atteindre nos objectifs est l'une de nos valeurs partagée par nos 4 600 salariés, répartis sur 5 centres. Les 1 800 salariés du centre de Bruyères-le-Châtel, en Ile de France relèvent les défis scientifiques et technologiques au service de notre Sécurité Nationale. Le centre conçoit les charges nucléaires des armes de la dissuasion, garantit leur sécurité et leur fiabilité en s'appuyant sur le programme simulation. Il met son expertise technique au service des activités dans la lutte contre la prolifération nucléaire, le terrorisme et les alertes en cas de séisme ou de tsunami. Il assure l'ingénierie des infrastructures complexes de la DAM, de leur conception à leur démantèlement. Il co-développe avec Atos les supercalculateurs au meilleur niveau mondial, dont sont issus ceux du Très Grand Centre de Calcul du CEA, qu'il exploite pour ses missions Défense et gère au profit de la recherche. Enfin, il exploite les installations nécessaires au maintien en condition opérationnelle et à la conception des chaufferies nucléaires embarquées sur les sous-marin et les porte-avions.
Venez-vous investir et relever des défis avec des moyens technologiques d'exception
Référence
S1904
Domaine
Physique théorique
Contrat
Post-doctorat
Intitulé de l'offre
Post-doctorat - Machine learning based MD for two temperature metals - H/F
Sujet de stage
The advent of femtosecond lasers has shed new light on non-equilibrium physics. The rapid energy absorption by electrons and their subsequent energy transfer to the lattice results in non-equilibrium states of matter, initiating a new class of non-thermal processes from ambient solids to extreme conditions of temperature and pressure. The dynamic interplay between electrons and the atomic structure is the central issue driving ultrafast phase transitions. However, the time scale of phase transitions and the microscopic mechanism driving melting are still not well understood. Classical molecular dynamics is well-suited to address this question, but classical potentials are limited in their ability to describe phenomena induced by electronic structure. DFT-based molecular dynamics could overcome this limitation, but it cannot reach the number of atoms necessary to provide a realistic picture. Machine learning potentials fitted on DFT simulations can bridge this gap.
Durée du contrat (en mois)
2 ans + 2 x 1an renouvelables
Description de l'offre
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.
Profil du candidat
The ideal candidate will have a robust background in either condensed matter physics or plasma physics. Additionally, experience with molecular dynamics simulation is highly desirable. Good skill in programming languages such as C++ and Python is also required.
DFT based MD code Abinit, Classical MD code ExaStamp.
Post-doctorat
Site
DAM Île-de-France
Localisation du poste
France, Ile-de-France
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