In-situ 4D tracking of microstructural evolution in atomistic simulations
The exponential growth of high-performance computing has enabled atomistic simulations involving billions or even trillions of particles, offering unprecedented insight into complex physical phenomena. However, these simulations generate massive amounts of data, making storage and post-processing increasingly restrictive. To overcome this limitation, on-the-fly (in-situ) analysis has emerged as a key approach for reducing stored data by extracting and compressing relevant information during runtime without significantly affecting simulation performance.
In this context, tracking the four-dimensional (space and time) microstructural evolution of materials under extreme conditions is a major scientific challenge. Atomistic simulations provide a unique spatial resolution to analyze crystalline defects such as dislocations, twinning, vacancies and pores, which govern dynamic phase transformations, melting, damage and mechanical behavior. By capturing their spatio-temporal evolution, it becomes possible to study their statistics, correlations and collective effects in out-of-equilibrium regimes, leading to more accurate predictive material models.
This project builds on advances of the exaNBody high-performance computing platform and a recently developed in-situ clustering method in the ExaStamp molecular dynamics code at CEA. This method projects atomic information onto a 3D Eulerian grid to perform real-time clustering. The objective is to extend this approach to full 4D tracking, enabling the time-resolved monitoring of clusters. This will allow dynamic graph-based analysis of their evolution, including changes in size, shape and temporal behavior, providing new insights into microstructural dynamics at the atomic scale.
Spin-lattice interactions in Machine Learning assisted ab initio simulations
The scientific field addressed by this postdoctoral project lies at the intersection of ab initio molecular dynamics, machine learning, and the thermodynamic characterization of materials under extreme conditions. Traditional AIMD simulations are a powerful tool to study temperature- and pressure-dependent properties from first principles, but their high computational cost limits their widespread use. By developing and applying machine learning-assisted sampling techniques like MLACS, this postdoc aims to drastically reduce the computational burden while retaining ab initio accuracy. This enables the efficient exploration of phase diagrams in high-pressure and high-temperature conditions. This research contributes to both fundamental understanding and practical modeling of materials, offering high-impact tools for the scientific community.
Bayesian inference-based ab initio phase diagrams
The scientific field addressed by this postdoctoral project lies at the intersection of ab initio molecular dynamics, machine learning, and the thermodynamic characterization of materials under extreme conditions. Traditional AIMD simulations are a powerful tool to study temperature- and pressure-dependent properties from first principles, but their high computational cost limits their widespread use. By developing and applying machine learning-assisted sampling techniques like MLACS, this postdoc aims to drastically reduce the computational burden while retaining ab initio accuracy. This enables the efficient exploration of phase diagrams in high-pressure and high-temperature conditions. This research contributes to both fundamental understanding and practical modeling of materials, offering high-impact tools for the scientific community.
Method for dimensionality reduction applied to deformed coupled cluster theory
Ab initio calculations in nuclear physics have undergone considerable progress over the past 20 years, enabling the study of several hundred nuclei with approximately 5% precision, notably through the PAN@CEA collaboration (A-Nucleon Problem at CEA) between DAM, DRF, and DES. These methods connect nuclear phenomenology to QCD theory via chiral effective field theory (cEFT) and find applications in both nuclear structure and particle physics.
Despite these advances, the majority of the Segrè chart remains inaccessible, with limitations to nuclei of mass A~100. This limitation stems from the computational and memory costs that scale with the desired mass and precision, related to the storage of large tensors. Recent research has demonstrated that a significant portion of the information in these tensors can be compressed through dimensionality reduction methods without significant loss of precision.
The postdoctoral project aims to extend these methods to the non-perturbative framework of deformed coupled cluster theory (dCC). The objectives are: 1) to implement the dCCSD method for nuclei up to A~80, 2) to develop its factorized version (TF-dCCSD) and validate it, 3) to extend it either to excited states (EOM-dCCSD) or to sub-percent precision (dCCSDT).
Simulation of landslides and the associated water waves by a 3D code
Until now, tsunamis generated by underwater landslides were modelled at the CEA using a 2D long wave code (Avalanche) that was adapted to the computing resources available at the time but now seems obsolete in the literature. An initial post-doctoral study (2023-2025) showed that the 3D OpenFoam tool could accurately simulate a landslide and the associated waves in the generation zone. During this post-doctoral fellowship, a coupling between the CEA's ‘2D’ propagation code (Taitoko) and the 3D code was developed in order to propagate waves over long distances. The work carried out will be continued. The first objective will be to familiarise with the tools developed and to publish the work carried out on the 80 Mm3 collapse that occurred in Mururoa in 1979. The main objective is then to carry out simulations of potential collapses in the northern zone, bearing in mind that the main difficulty lies in defining the geometry of these potential collapses. The propagation of waves over long distances is simulated by a ‘2D’ tsunami code coupled with the OpenFoam code.
Multipoint initiation of explosives. Experiments and simulations.
The design of high-performance and increasingly safe systems requires new solutions for initiating the explosives that make up their charge. One possible approach is to replace the electrical ignition of detonators with optical ignition in order to eliminate the risks associated with parasitic electrical sources.
Another possible way to improve safety is to use multipoint initiation so that the explosive only detonates when all initiation points are activated synchronously.
The objective of the postdoctoral contract will be to conduct an in-depth study of the mechanisms governing multipoint optical initiation. To this end, after conducting exhaustive bibliographic research, the candidate will propose the most relevant configurations and test them both experimentally and by performing hydrodynamic simulations using a code developed at the CEA. Understanding the phenomena involved is essential in order to be able to choose an initiation configuration suited to each need.
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.
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.