Solid-electrolyte developpement
This post-doctorate position is a part of the ArgyL Carnot project which aims at developping better solid électrolytes for Li batteries. The main goal is to stabilize the Li/electrolyte interface in order to increase the lifetime of these systems that promise better Energy Density than the actual ones.
This project relies on the complementarity between the experimental approach based on the synthesis and on Advanced characterization and the computational simulations.
The post-doctorate will be based at the Materials laboratory where he will be in charge of synthesizing the new electrolyte compositions. He will also take part in the development of a new operando XPS analysis technique on our Nanocharaterization platform. This technique will allow him to probe in real time the interface between Li and electrolyte during cycling.
Finally, he will also interact with the LMPS simulation team who will use the data to feed their ab initio model.
Generative deep-learning modeling and machine-learning potentials for the calculation of atomic-scale transport properties in disordered uranium-plutonium mixed oxides
Machine learning (ML) is now commonly used in materials science to enhance the predictive capabilities of physical models. ML interatomic potentials (MLIP) trained on electronic-structure calculations have become standard tools for conducting efficient yet physically accurate molecular dynamics simulations. More recently, unsupervised generative ML models are being explored to learn hidden property distributions, and generate new atomic structures according to these distributions. This is useful for chemically disordered solid solutions, whose properties depend on the distribution of chemical species in the crystal lattice. In such cases, the number of possible configurations is so large that exhaustive sampling is beyond the capability of conventional methods. An example is U-Pu mixed oxides (MOX), a type of nuclear fuel that can significantly reduce the volume and radiotoxicity of spent-fuel waste. High-entropy alloys are another class of disordered materials that are promising candidates for radiation-resistant nuclear materials.
The goal is to combine MLIPs and generative methods to address atomic transport properties in MOX. The candidate will use in-house ML generative tools to generate representative atomic configurations and build an ab initio database. They will use this database to train a new MLIP for MOX, leveraging the experience gained from developing analogous MLIPs for the pure UO2 and PuO2 oxides. Finally, they will apply the MLIP to calculate atomic diffusion coefficients, crucial for predicting irradiation-induced microstructure evolution and in-reactor behavior.
The work will be conducted at the Nuclear Fuel Department (IRESNE, CEA Cadarache), within a scientific environment characterized by a high level of expertise in materials modelling, and in close collaboration with other CEA teams in the Paris region specialized in ML methods. Results will be disseminated through scientific publications and participation in international conferences
Simulation of surfaces and interfaces between halogenated perovskites and carrier transport layers.
Context: Halide perovskites are a very active field of research for their applications in photovoltaics (PV), light emitting devices, X-ray detectors, and more. The role of interfaces in the devices, especially in enhancing carrier extraction in solar cells, is crucial. Atomic scale modelling of bulk and surfaces of these
materials is challenging, because of their softness, associated with anharmonic behaviour and local atomic disorder (polymorphism), spin-orbit coupling and polar distortions. In spite of recent advances in describing polymorphism and anharmonic lattice dynamics in the bulk, studies of surface and interface properties are recent and still rare.
Description and duties: The work will focus on building predictive models of interfaces between pérovskites light absorbers and electron/holes transport layers (ETL/HTL) based on Density Functional Theory. Existing and new HTL/ETL materials will be investigated with a special focus on understanding passivation mechanisms, defects at the interface, their stability and kinetics. Development of machine learning potential is envisaged.
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.
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.
Development of a new atomic reference database for radioactive processes
Several scientific communities have highlighted the lack of precision and the inconsistencies present in the reference atomic database EADL. The data were calculated using a fairly simple Dirac-Hartree-Slater approach and then subsequently corrected empirically. However, to date it remains the only database that is sufficiently complete to be usable by simulation codes. In recent years, a collaboration was initiated and reinforced during two successive European projects between the FCT-UNL (Lisbon, Portugal), the IPCMS (Strasbourg, France) and the LNHB (CEA Saclay, France). A new relativistic atomic code, based on the density functional theory, has been developed and validated by studying different electron capture transition probabilities. The aim of the present subject is to develop a new reference atomic database based on this atomic code. The required theoretical equations will have to be established. Several elements will be calculated and the predictions will be compared to available results in the literature. The influence of this precise atomic modeling on the atomic exchange effect that occurs in beta transitions will also be studied. At least one publication and one participation to an international conference are expected.
Improvement and extension of a phase-field model for the 3D simulation of important phenomena in the behavior of lithium-ion batteries
In order to optimize the charging time of current-generation batteries, or increasing the power density for future generations, the study of the behavior of materials is crucial to master the lithiation mechanisms of intercalations materials. (e.g. graphite) or “stripping/plating” of lithium metal. In this context, the use of phase field numerical simulations is booming; these methods lend themselves to the modeling of dynamic phenomena for multiphase and multiconstituent systems.
Recently, a 2D phase field module from TrioCFD (open-source software developed at CEA and based on the TRUST platform) was generalized to an arbitrary number of constituents or phases. This post-doctoral project aims to improve and extend this TrioCFD module to high-performance 3D simulations in a distributed parallel computing environment. The objective is to use this module to simulate the 3D physical behaviors of interest of the aforementioned lithium-ion battery materials. We will rely on recent 2D phase field work which has provided a certain number of original and relevant answers to these issues. The move to 3D simulations will provide essential scientific perspectives for these applications.
This work will be carried out as part of a collaboration between several CEA teams from the Cadarache, Grenoble and Saclay centers, bringing together varied expertise (behavior of lithium-ion batteries, phase field method, TrioCFD software environment and numerical methods).
Plastic recycling enabled by toxic additives extraction using green solvents
It is important to develop the scientific knowledge and stimulate innovations to recycling Plastics. The extremely large variety of plastic based objects that we use in our daily life are made of a wide range of plastic materials covering many different polymers, many different formulations. Plastics objects are also used for many purposes and there is therefore the need of various ways to collect, sort and treat them.
Methods of recycling of plastics are generally divided into four categories: primary, secondary, tertiary, and qua-ternary (see Figure 9). Primary recycling or closed loop recycling method is considered when the materials after recycling present equal or improved properties compared to the initial or virgin materials. When the recyclates present a decrease in the properties level, one may spook about secondary or down-cycling method. In tertiary (also known as chemical or feedstock) recycling method, the waste stream is converted into monomers or chemicals that could be advantageously used in the chemical industries. Finally, quaternary (also known as thermal recycling, energy recovery, and energy from waste) recycling method corresponds to the recovery of plastics as energy and is not considered as recycling for Circular Economy.
Various processes can be considered for chemical recycling which present different level of maturity. Hence this project that will study the decontamination of various PVC formulations using green solvents, and more particularly supercritical CO2
This work located in Saclay, France, in the heart of the University Paris-Saclay and will benefit from a very multidisciplinary and international environment.
This work will benefit from the prestigious framework of the France 2030 funding, and more precisely the PEPR Recycling (https://www.cnrs.fr/fr/pepr/pepr-recyclabilite-recyclage-et-reincorporation-des-materiaux-recycles ). It will be supervised by Dr. Jean-Christophe P. Gabriel: linkedin.com/in/jcpgabriel).
Hybrid ion exchangers for the traetment of radioactive organic liquids: molecular dynamics design assistance
The ECCLOR project (labelled 'Investissement pour le future') focuses on the treatment of radioactive organic effluents by developing porous materials capable of selectively eliminating alpha emitting ions. Research carried out at CEA has led to the design of hybrid materials with variable performance in capturing alpha emitters present in organic liquids. Understanding this performance at the molecular level is essential, but complex.
To address this challenge, this post-doctoral fellowship focuses on the use of classical molecular dynamics to rationalize these performances. The work will be carried out at the Marcoule research center's LILA laboratory, drawing on the expertise of teams specializing in the modeling of solid/liquid systems using classical molecular dynamics.
To support these simulations, experimental data may be provided by laboratories such as the Laboratoire des Procédés Supercritiques et de Décontamination (LPSD) and the Laboratoire de Formulation et Caractérisation des Matériaux minéraux (LFCM). The results obtained will be discussed at progress meetings and will be the subject of scientific publications.
In summary, this post-doctoral contract aims to couple theoretical approaches with experiment. Understanding the interactions within these materials at the molecular scale is essential to provide insights and improve the processes currently under study.
Predicting corrosion properties using artificial intelligence and thermodynamic calculations
Project CORRTHIA aims to demonstrate the use of thermodynamic calculation for predicting corrosion properties using artificial intelligence (AI) models. Given the complexity of corrosion and the difficulty in describing it with current physical models,
understanding a material's behavior requires long-term experiments. The use of AI for predicting corrosion properties is a promising method for accelerated materials design, as it can limit the required experiments by selecting relevant materials. This research
theme aligns with the CEA's strategy for numerical material design and the objectives of the PEPR DIADEME.
Leveraging expertise in corrosion and thermodynamic calculation from the DES team and AI experience from DRT partners, we will focus on high-temperature oxidation (> 500°C) of metallic alloys to limit the range of compositions and possible environments. It is
expected that thermodynamic aspects play an essential role under these conditions. The scarcity of corrosion data necessitates work on constructing a dataset (Lot 1), based on published literature or internal S2CM data, which will be enriched with thermodynamic
calculation results. This augmented dataset will be used to train AI models.