Generative Deep-learning Modeling of Atomic
il y a 2 semaines
Position description
**Category**:
- Materials, solid state physics
**Contract**:
- Postdoc
**Job title**:
- Generative deep-learning modeling of atomic transport in chemically disordered nuclear fuels H/F
**Subject**:
- The goal of the project is to use deep-learning generative modeling to study atomic-transport phenomena in chemically disordered compounds such as uranium-plutonium mixed oxides, a type of nuclear fuel that allows for a drastic reduction of the volume and radiotoxicity of spent-fuel waste.
**Contract duration (months)**:
- 12
**Job description**:
- Machine learning is nowadays of common use in materials science to improve the predictive capabilities of physical models. Machine learning interatomic potentials (MLIP) trained on electronic-structure calculations have become the standard tools for running efficient yet physically accurate molecular dynamics simulations. More recently, generative deep-learning models are explored to learn a hidden property distribution in an unsupervised manner, and generate new structures according to this hidden distribution. This is of particular interest for chemically disordered solid solutions, whose properties depend on the distribution of chemical species in the crystal lattice. In this case, the number of possible configurations is so high that an exhaustive sampling of this space is beyond the reach of conventional methods. An example is given by uranium-plutonium mixed oxides (MOX), a type of nuclear fuel that allows for a drastic reduction of the volume and radiotoxicity of spent-fuel waste.-
- The work will be carried out at the Nuclear Fuel Department of the CEA, under the supervision of Luca MESSINA and Johann BOUCHET, in a scientific environment characterised by a high level of expertise in materials modelling. Other CEA teams in the Paris region specialised in machine-learning methods will be involved as well, including Jean-Bernard MAILLET and Cosmin MARINICA. The results will be promoted through scientific publications and participation in international conferences.- References:
**Methods / Means**:
- Deep learning, autoencoders, DFT, LAMMPS, MiLaDy
**Applicant Profile**:
- A base knowledge of machine-learning techniques, good programming skills, and previous experience in atomic-scale modeling are necessary.Position location
**Site**:
- Cadarache
**Job location**:
- France, Provence-Côte d'Azur, Bouches du Rhône (13)
**Location**:
- Saint-Paul-lez-Durance
**Recommended training**:
- PhD in solid state physics/chemistry or applied mathematics/machine learning
**PhD opportunity**:
- Non
Requester
**Position start date**:
- 01/03/2023
General information
**Organisation**:
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas:
- defence and security,
- nuclear energy (fission and fusion),
- technological research for industry,
- fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
- The CEA is established in ten centers spread throughout France**Reference **:2023-25779**Description de l'unité**:
- Le Département d'Etudes des Combustibles (DEC) réalise la R&D sur le combustible des réacteurs nucléaires en regroupant des compétences uniques:
- en conception/réalisation d'essais caractérisant le comportement du combustible en conditions normales ou accidentelles par des essais en laboratoires chauds LECA/STAR ou plus intégrés dans des réacteurs de recherche (RR) ;
- en simulation avec des modèles multiéchelles, multiphysiques et multifilières (plateforme PLEIADES).Le point fort du département est d'associer étroitement la simulation numérique aux expériences, à toutes les échelles
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