Development of explosive compositions using RAM acoustic technology
The objective is to develop, at the laboratory scale, formulations consisting of one or more energetic molecules and a binder. These materials must match the specifications in terms of performance and insensitivity.
This new process using acoustic waves is an innovative and very promising method for rapidly obtaining new explosive compositions with optimized properties. The work will therefore consist of:
- Conducting a bibliographic review to obtain formulations in granular form using the RAM (resonant acoustic mixing) process.
- Identifying the various process parameters and understanding their influence on the final properties of the mixture.
- Formulating energetic compositions on a laboratory scale that match the specifications.
- Characterizing the materials produced through physicochemical analyses.
- Proposing improvements to optimize the final properties.
- Proposing new applications for RAM in connection with the unit's activities.
Synthesis of high-nitrogen heterocyclic molecules
One of the CEA DAM's objectives is the design of new explosive compositions with optimized properties. As such, the search for new molecules of interest, likely to be integrated into innovative formulations, is a fundamental activity.
The objective of the post-doctorate is to synthesize, on a laboratory scale, energetic molecules with structures capable of meeting the specifications in terms of performance and insensitivity. These are mainly highly nitrogenous heterocyclic molecules (pyrazoles, triazoles, oxadiazoles, etc.). The work will include both the synthesis of intermediates, whether they are considered energetic or not, and that of the final products.
This approach is supported by modeling work carried out upstream, intended to set up tools to propose new structures and evaluate their properties by calculation. This subject will require, in interaction with the modeling team, using these tools and putting them to good use to guide the choice of targets that will be studied experimentally in the laboratory.
Optimizing chemical reactivity with interpretabe machine learning
In organic synthesis, many molecular and macroscopic parameters can influence the outcome of chemical reactions. It is therefore difficult to correlate the obtained yields with the reaction conditions. This project aims to develop interpretable machine learning models to predict and improve the efficiency of oxidation reactions of electron-deficient heterocycles, a real challenge in organic chemistry. The main challenge will be to best represent and leverage the variables associated with the complexity of a real reaction system (chemical nature of the substrate, temperature, reaction time, etc.) to feed machine learning algorithms and extract clear rules. The ultimate goal is to provide chemists with predictive tools to rationalize and develop these transformations.
Development and characterization of an oxide/oxide composite material
Fiber-reinforced ceramic matrix composites (CMCs) are a class of materials that combine good specific mechanical properties (properties relative to their density) with excellent high-temperature resistance (> 1000 °C), even in an oxidizing atmosphere. They generally consist of a carbon or ceramic fiber reinforcement and a ceramic matrix (carbide or oxide).
The proposed study focuses on the development of a fabrication process for oxide/oxide CMCs with long and/or short fibers that possess suitable dielectric, thermal, and mechanical properties.
AI : modelisation and scaling in laborator astrophysics
In astrophysics, accreting systems produce X-ray sources commonly observed by satellites and ground
based telescopes. Spectral signatures allow us to deduce the mass, magnetic field, accretion rate, and
chemical composition of the star and structures. However, these structures are found in very small spatial
areas and are not resolved by observational tools. Laboratory astrophysics allows us to miniaturize these
processes and study them through experiments using high-power lasers. These experiments allow for the
characterization of the plasma and its spatial structuring.
The postdoctoral fellow will exploit the possibilities of using physically informed neural networks to study the possibility of extrapolating radiative hydrodynamic simulation results. He will develop a tool to simply determine the relevant materials and regimes for sizing laboratory experiments. Finally, he will use AI to try to find scaling law relationships between two systems.
Particle-in-cell modeling of elastic collisions in dense and cold plasmas with applications to ultrafast beam-plasma and laser-plasma interactions
The particle-in-cell (PIC) method is widely employed to simulate the kinetics of plasmas subjected to intense laser or particle beams. Modern PIC codes now routinely include additional modules describing atomic physics processes, but their accuracy is questionable in relatively cold (at temperatures below a few tens of eV) and dense (close to the density of a solid) media.
This postdoctoral project aims to improve the treatment of elastic collisions in PIC simulations by drawing on transport theories derived for liquid metals and dense plasmas, i.e. by taking into account electronic degeneracy, electronic screening and atomic ordering effects. This model will be implemented into the PIC CALDER code developed at CEA. Once validated, this new model will be used in PIC simulations to examine its influence in setups involving solid targets exposed to ultraintense and ultrashort electron or laser beams.
Excited electronic states in the GW Approximation coupled to the Projector Augmented-Wave Approach
This project aims to address a major gap in ab initio calculations by enabling reliable simulations of excited electronic states (GW method) using the Projector Augmented-Wave (PAW) approach. These advances will be integrated into the open-source software ABINIT, a recognized international collaborative project. The GW approximation is considered the gold standard for determining electronic energy levels in condensed matter, correcting the underestimated band gap in DFT. The PAW method, on the other hand, offers precision and flexibility and is widely used for ground state and material response calculations.
However, the combined GW+PAW approach encounters difficulties in some well-identified cases (e.g., zinc oxide), with underlying reasons understood but not yet fully resolved. Low-energy excited states are well described, but high-energy states remain problematic. The current debate focuses on the need to perform complete (but computationally expensive) calculations, to neglect certain terms (with complex error control), or to modify the PAW method (at the cost of reduced efficiency).
The project aims to adapt the PAW formalism to the GW approach, to develop a fast and accurate numerical scheme, and to clarify the current, somewhat confusing situation. The CEA team is a leading developer of ABINIT for PAW and GW and will ensure access to large computing resources. The postdoctoral objectives include theoretical development, implementation in ABINIT, and improving electronic properties for realistic solid systems (surfaces, semiconductor junctions, etc.).
Dimensionality reduction and meta-modelling in the field of atmospheric dispersion
Modelling and simulation of atmospheric dispersion are essential to ensure the safety of emissions emitted into the air by the authorized operation of industrial facilities and to estimate the health consequences of accidents that could affect these facilities. Over the past twenty years, physical dispersion models have undergone significant improvements in order to take into account the details of topography and land use that make real industrial environments complex. Although 3D models have seen their use increase, they have very significant calculation times, which hinders their use in multi-parametric studies and the assessment of uncertainties that require a large number of calculations. It would therefore be desirable to obtain the very precise results of current models or similar results in a much shorter time. Recently, we have developed a strategy consisting of reducing the dimension of distribution maps of an atmospheric pollutant obtained using a reference 3D physical model for different meteorological conditions, then having these maps learned by an artificial intelligence (AI) model which is then used to predict maps in other meteorological situations. The postdoctoral project will focus on complementing the research started by evaluating the performance of dimension reduction and model substitution methods already explored and by studying other methods. Applications will concern, in particular, the simulation of concentrations around an industrial production site that emits gaseous emissions into the atmosphere. The developments will aim to obtain an operational meta-modelling tool.
Preparation and characterization of an oxide/oxide composite
Fiber-reinforced ceramic matrix composites (CMCs) are a class of materials that combine good specific mechanical properties (properties relative to their density) with resistance to high temperatures (> 1000 °C), even in oxidizing atmospheres. They are typically composed of a carbon or ceramic fiber reinforcement and a ceramic matrix (carbide or oxide.
The proposed study focuses on the development of a low-matrix oxide/oxide CMC with suitable dielectric, thermal, and mechanical properties.
This study will be conducted in collaboration with several laboratories at CEA Le Ripault.
Influence of laser bandwidth and wavelength on laser plasma instabilities
As part of the Taranis project initiated by Thales and supported by BPI France and in collaboration with numerous scientific partners such as CEA/DAM, CELIA and LULI, work on target design and definition of the laser intended to energy production in direct drive will take place. A prerequisite for this work is to understand the laser-plasma interaction mechanisms that will occur when the laser is coupled with the target. These deleterious mechanisms for the success of fusion experiments can be regulated by the use of so-called “broadband” lasers. In addition, the choice of the laser wavelength used for the target design and the laser architecture must be defined. The objective of the postdoctoral position is to study the growth and evolution of these instabilities (Brillouin, Raman) in the presence of “broadband” lasers both from an experimental and simulation point of view, and thus to be able to define the laser conditions making it possible to reduce these parametric instabilities.