Molecular Dynamics study of deformation and phase transition mechanism in tin

Several pressure-induced phase transformations have been predicted and observed in tin; its phase diagram reflects its special position in group IV of the periodic table of elements, where the lighter elements (C, Si, Ge) tend to form covalent bonds. The most stable phase at 0K corresponds to a diamond phase similar to those found in lighter elements. However, pressure and temperature transitions are observed, associated with a change in the nature of the interatomic bonds. The thermodynamic and mechanical properties of the different phases of tin, as well as the structural transitions, are fairly well known today, but are still difficult to reproduce using electronic structure calculations.

For classical Molecular Dynamics (MD) simulations, a number of semi-empirical potentials have been proposed in the literature, which can reproduce certain parts of the phase diagram or certain properties, but which are limited in their ability to predict certain properties, in particular the elastic constants. Recently, Machine Learning Interatomic Potentials (MLIPs) have been developed to improve the description of the properties of the different crystalline phases. However, these potentials, trained on crystalline phases at temperature and liquid configurations, do not take into account the specific distortions of the lattices encountered during deformation of the material (dislocation formation, maculation).

Thermodynamic study of photoactive materials for solar cells

The development of solar photovoltaic electricity generation requires the development of new materials for converting solar radiation into electron-hole pairs. Organic-inorganic hybrid perovskites (HOIPs) of the CsPbI3 type, with substitutions of Cs by formamidinium (FA) and/or methylammonium (MA) ions, have emerged as very promising materials in terms of performance and manufacturing. Substitutions of Cs with elements such as Rb, Pb with Sn, and I with Br are also being considered to improve stability or performance. The synthesis and optimization of the composition of layers of such materials require a better understanding of their thermodynamic equilibrium properties and stability. The objective is to construct a thermodynamic model of the Cs-Rb-FA-Pb-Sn-I-Br system. The project began with the ternary Cs-Pb-I system, which resulted in a paper [1]. The next step will focus on the ternary Cs-Pb-Br system, followed by the quaternary Cs-Pb-I-Br system. The approach uses the CALPHAD method, which focuses on building a database and an analytical formulation of the phases Gibbs energy, capable of reproducing thermodynamic and phase diagram data. A critical review of the data in the literature will enable this database to be initialized and the missing data will be evaluated by experiments and/or DFT calculations.

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

Accelerated development of materials resistant to molten chloride salts

The accelerated development of materials is a major challenge for all industries, and corrosion resistance is all the more important for resource conservation issues. This project therefore aims to estimate the corrosion resistance of FeNiMnCr alloys in chloride salt for use in molten salt nuclear reactors, in collaboration with the University of Wisconsin, which has demonstrated extensive expertise in the accelerated development of materials for molten fluoride and chloride salt reactors. As part of this post-doc, dozens of samples of quaternary FeNiMnCr model alloys will be synthesised by additive manufacturing at the University of Wisconsin, varying the composition in order to map the entire composition tetrahedron as accurately as possible. These samples, with a NiCr model alloy corroded in a wide range of molten chlorides salt chemistries, will then be corroded at the CEA. The aim of these experiments is, on the one hand, to obtain a large database on the corrosion of FeNiMnCr alloys in a very short time (1.5 years) and, on the other hand, to screen the effect of a wide range of salt compositions on a model NiCr alloy. Finally, these experiments will make it possible to target the best materials for studying their corrosion mechanisms.

Study of the Thermodiffusion of Small Polarons in UO2

The position is published on the CEA website at the following address:
https://www.emploi.cea.fr/job/emploi-post-doctorat-etude-en-ab-initio-de-la-thermodiffusion-des-petits-polarons-dans-UO2-h-f_36670.aspx

Impact of Microstructure in Uranium Dioxide on Ballistic and Electronic Damage

During reactor irradiation, nuclear fuel pellets undergo microstructural changes. Beyond 40 GWd/tU, a High Burnup Structure (HBS) appears at the pellet periphery, where initial grains (~10 µm) fragment into sub-grains (~0.2 µm). In the pellet center, under high temperatures, weakly misoriented sub-grains also form. These changes result from energy loss by fission products, leading to defects such as dislocations and cavities. To study grain size effects on irradiation damage, nanostructured UO2 samples will be synthesized at JRC-K, using flash sintering for high-density pellets. Ion irradiation experiments will follow at JANNuS-Saclay and GSI, with structural characterizations via Raman spectroscopy, TEM, SEM-EBSD, and XRD. The postdoc project will take place at JRC-K, CEA Saclay, and CEA Cadarache under expert supervision.

Development of new Potassium-ion cells with high performances and low environmental impact

Lithium ion batteries are considered as the reference system in terms of energy density and cycle life and will play a key role in the energetic transition, especially concerning electric vehicles. However, such a technology involves the use of a large amount of critical elements and active materials are synthesised using energy intensive processes.
In this way, our team is developing a new Potassium-ion batteries technology with high performances but with a low environmental impact.
For this innovative and ambitious project, CEA-LITEN (one of the most important research institute in Europe) is looking for a talented post-doctoral researcher in material chemistry. The post-doctoral position is opened for a young researcher with a high scientific level, interested by valorising her/his results through different patents and/or scientific publications.

Calculation of the thermal conductivity of UO2 fuel and the influence of irradiation defects

Atomistic simulations of the behaviour of nuclear fuel under irradiation can give access to its thermal properties and their evolution with temperature and irradiation. Knowledge of the thermal conductivity of 100% dense oxide can now be obtained by molecular dynamics and the interatomic force constants[1] at the single crystal scale, but the effect of defects induced by irradiation (irradiation loop, cluster of gaps) or even grain boundaries (ceramic before irradiation) remain difficult to evaluate in a coupled way.
The ambition is now to include defects in the supercells and to calculate their effect on the force constants. Depending on the size of the defects considered, we will use either the DFT or an empirical or numerical potential to perform the molecular dynamics. AlmaBTE allows the calculation of phonon scattering by point defects [2] and the calculation of phonon scattering by dislocations and their transmission at an interface have also recently been implemented. Thus, the chaining atomistic calculations/AlmaBTE will make it possible to determine the effect of the polycrystalline microstructure and irradiation defects on the thermal conductivity. At the end of this post-doc, the properties obtained will be used in the existing simulation tools in order to estimate the conductivity of a volume element (additional effect of the microstructure, in particular of the porous network, FFT method), data which will finally be integrated into the simulation of the behavior of the fuel element under irradiation.
The work will be carried out at the Nuclear Fuel Department of the CEA, in a scientific environment characterised by a high level of expertise in materials modelling, in close collaboration with other CEA teams in Grenoble and in the Paris region who are experts in atomistic calculations. The results will be promoted through scientific publications and participation in international congresses.
References:
[1] Bottin, F., Bieder, J., Bouchet, J. A-TDE

Automatic machine learning identification of nanoscale features in transmission electron microscopy images

Imaging nanoscale features using transmission electron microscopy (TEM) is key to predicting and assessing the mechanical behaviour of structural materials in nuclear reactors or in the fields of nanotechnology. These features, visible by phase contrast (nanobubbles) or diffraction contrast (dislocation loops or coherent precipitates), are prime candidates for automation. Analysing these micrographs manually is often tedious, time-consuming, non-universal and somehow subjective.

In this project, the objective is to develop a Python-based framework for data treatment of transmission electron microscopy (TEM) images.
Machine Learning approaches will be implemented in order to tackle the following tasks:
- Data collection: The success of any machine learning approach is linked to the database quality. In this project, a huge database is available. Four microscopists are involved in the project and will continuously enrich the database with images containing easily recognizable features.
- Denoising and finding the defect contour both through existing open-access software and in-house developed descriptors. Representative ROI (region-of-Interest) will be generated on images.
- Design of the Convolutional Neural Network (CNN) Architecture and model training: A collective feature map will be generated for the entire images in order to identify some representatives ROI. Each ROI is then overlaid to the original feature map and is passed to the CNN for individual region classifications. Secondly, recent advances in image segmentation will be placed in the core engine of the workflow.
- Model performance metrics: The aim is to reach a compromise between the training time and the detector performance.
The process will be applied to nanometer-sized features formed under irradiation in nuclear oriented materials (Co-free high entropy alloys (HEA), UO2) and precipitates in materials with a technological interest (coherent Cr precipitates in Cu).