AI-Driven Multiscale Simulation of Ferroelectric Switching in Hafnium Oxides
What happens at the atomic scale when a bit of information is written in a ferroelectric memory, and how does it determine the behaviour of a real device? This PhD will address these questions by combining machine learning, large-scale atomistic simulations and device-scale modelling.
Ferroelectric Hafnium Zirconium Oxide (HZO) has become a key material for ultra-low-power non-volatile memories such as FeRAM and ferroelectric transistors (FeFETs). It is CMOS-compatible and remains ferroelectric in films only a few nanometres thick. Yet its ferroelectric behaviour is still only partly understood. It depends strongly on composition, defects, microstructure and interfaces, and it is governed by switching mechanisms at the nanoscale. Density functional theory (DFT) is highly accurate but limited to a few hundred atoms, far too few to capture these mechanisms. Machine-learning interatomic potentials, trained on DFT calculations, now overcome this limit. They can simulate the response of materials to an electric field with near-DFT accuracy on systems of up to a million atoms.
Building on our in-house machine-learning framework, you will first run large-scale molecular dynamics simulations of ferroelectric switching in HZO films and in complete TiN/HZO/TiN capacitors under applied voltage. You will study domain nucleation, domain-wall motion and the role of oxygen vacancies and electrode interfaces. You will then use these results to parameterize a device-scale model of the full memory cell, in order to interpret electrical measurements on FeRAM devices fabricated at CEA-Leti. You will work closely with the CEA-Leti teams in charge of FeRAM integration and advanced characterization.
Development and Application of Time-Resolved Cathodoluminescence for Nanoscale Characterization of MicroLEDs
Context: MicroLEDs based on III-nitride semiconductors are promising for display and optoelectronic technologies. Their performance depends on carrier recombination and transport at the nanoscale, particularly near sidewalls, interfaces and defects. Time-Resolved Cathodoluminescence (TRCL) combines electron microscopy spatial resolution with time- and spectrally-resolved luminescence, enabling investigation of local carrier dynamics with nanometric spatial and sub-nanosecond temporal resolution. The thesis aims to develop TRCL methods for III-nitride microLEDs.
Work description: The candidate will develop TRCL measurements: optimize electron-beam energy, current and temperature; investigate carrier dynamics and the influence of sidewalls, interfaces and defects; process multidimensional TRCL datasets. The work will be performed within the PFNC platform at CEA-Leti, in interaction with researchers working on microLEDs.
Required skills: Strong background in solid-state physics and motivation to learn characterization and data analysis. Knowledge of luminescence is appreciated. The candidate will develop skills through academic collaborations and interaction with LETI partners.
3D imaging by electron tomography of PFAS-free electrodes for green hydrogen
To decarbonize our society, green hydrogen produced by water electrolysis using renewable electricity is a promising pathway. However, today's electrolyzers rely on fluorinated polymers (PFAS), which face upcoming restrictions in Europe due to their environmental impact. Fluorine-free alternatives, namely hydrocarbon (HC) ionomers, are making progress in membranes, but their use in electrodes still yields limited performance, most likely due to poor dispersion of the ionomer within the electrode.
The electrode under study is a nanoporous material made of carbon aggregates supporting the catalyst (Pt nanoparticles) and bound together by the ionomer. The spatial distribution of the ionomer plays a decisive role, as it must provide ionic conduction to the catalytic sites without obstructing gas transport. Visualizing this ionomer is a significant challenge, as it forms a thin film only a few nanometers thick covering the carbon. Our group has acquired unique expertise in imaging this ionomer using electron tomography (E-TOMO). In the case of fuel cells (PEMFC), where the ionomer is fluorinated, this approach has made it possible to understand its distribution within the electrode.
The objective of this PhD project is to build upon this expertise to provide the first 3D images of HC ionomer distribution in electrodes. The methodology will be optimized to reduce analysis times by leveraging recent advances in the field. Analyses performed on various electrodes will provide key insights to optimize their fabrication process, better understand their performance, and assess their stability. E-TOMO will thus play a central role in advancing the development of fluorine-free electrodes for AEMWE electrolyzers.
Fundamental study of crystallization mechanisms in borosilicate glasses: toward an understanding of the synergies between Gd and Rare Earth Elements
In France, nuclear power plants generates approximately 68% of the country’s electricity, with a very low carbon footprint. However, it produces various types of radioactive waste throughout its life cycle, including so-called high-level waste resulting from spent fuel reprocessing. These wastes are confined within a borosilicate glass matrix whose structure allows for the incorporation of a large number of chemical elements at the atomic level, ensuring excellent long-term performance properties. Future challenges facing the industry may lead to changes in nuclear fuels and their burn-up rates, as well as adaptations to processing and recycling methods, which could alter the nature and chemical composition of waste solutions intended for vitrification.
Among these elements, gadolinium (Gd) — used as a neutron absorber in nuclear fuels — is of particular interest due to its complex behavior in solid solution and its potential to form crystalline phases (e.g., silicate apatites) when interacting with other rare earth elements (La, Nd) or actinides (Am). These crystalline phases could profoundly alter the structure of the glassy matrix and affect the material’s properties (chemical durability, radiation resistance. This thesis therefore aims to understand and quantify the synergistic effects between gadolinium and other rare earth elements in simplified borosilicate glasses, in order to better understand the crystallization affinities among the various elements. It will thus enable the identification of the nature, structure, size, and concentration of the various phases that may form. Their structural changes induced by alpha irradiation will also be studied to simulate the effects of actinides, particularly the swelling of crystalline phases and its impact on the material.
The PhD student will benefit from the recognised skills of the host laboratory in glass formulation as well as the study of their physico-chemical properties. All of the resources made available will enable a global approach to the subject, working on a fast-growing topic with major industrial and societal implications. The experience acquired during this interdisciplinary work will be useful in the field of materials.
Towards an advanced modelisation of actinide oxides through dielectric aware machine-learning potentials
Actinide oxides, such as UO2, PuO2, and their mixed oxide(MOX), are fundational fuel materials for existing and next-generation nuclear reactors where their thermomechanical properties dictate fuel safety and operational performance. These materials are polar ionic crystals, and their dielectric properties therefore play a central role in their physical behavior. For instance, long range electrostatic interactions and dynamical polarizabilities shape the phonon spectrum, control the thermodynamic properties and impact the heat transport of the solids. From a simulation point-of-view, neglecting these effects risks systematic error in the prediction of properties that matter for fuel performance.
This project will develop and apply novel machine-learning atomistic simulation methodologies that explicitly incorporate dielectric properties into the description of actinide oxides, bridging a critical gap between current frameworks and the physic of polar materials.
In the first phase of this work, you will train a machine-learning interatomic potential with explicit long-range electrostatic capability for UO2, PuO2 and MOX. The potential will then be applied in large-scale finite-temperature simulations to quantify the impact of electrostatic interactions on elastic properties, thermal expansion, thermal conductivity and key thermodynamic properties such as heat capacity. The same potential will be used to compute infrared spectra, which are directly sensitive to the dynamic charge response of the material. Comparison with available experimental measurement will serve as an assessment of the accuracy of the atomistic model for actinide oxides.
Building on this framework, the second phase will extend the MLIP architecture to couple explicitly to external electric fields, enabling the direct simulation of polarizability derivatives and Raman-active vibrational modes. This will allow the computation of Raman spectra directly from molecular dynamics, providing a link between spectroscopic signatures and atomic structure.
The results produced at each stage of this project will constitute original contributions to the field and will be disseminated through publications in international peer-reviewed journals and presentations at international scientific conferences.
Beyond its scientific outcomes, the project will provide you with a versatile skill set applicable to many areas of materials science: ab initio electronic-structure calculations, the development of machine-learning interatomic potentials, classical molecular dynamics, and the key concepts of statistical physics and condensed-matter physics. You will also receive training in high-performance computing, with direct access to CEA supercomputing facilities. The supervising team includes recognized experts in all of these fields.
You will join the Fuel Behavior Modeling Laboratory at the Research Institute for Nuclear Systems for Low-Carbon Energy Production (IRESNE, CEA Cadarache), a dynamic research team where you will have regular opportunities to interact with fellow PhD students and researchers. This environment also provides extensive opportunities for national and international collaboration, including with:
• Experimental researchers from the Nuclear Fuel Studies Department,
• Teams from other CEA centers (Saclay, CEA/DAM),
• International partners.
This rich and multidisciplinary context will enable you to fully engage with the scientific community focused on nuclear materials science.
[1] Dubois, E. T., Tranchida, J., Bouchet, J., & Maillet, J. B. (2024). Atomistic simulations of nuclear fuel UO2 with machine learning interatomic potentials. Physical Review Materials, 8(2), 025402.
[2] King, D. S., Kim, D. Zhong, P., & Cheng, B. (2025). Machine learning of charges and long range interactions from energies and forces. Nature Communications 16, 8763.
(U, Pu)O2 fuel for 4th generation reactors: the impact of the porosity on the thermal conductivity.
Thermal conductivity is a key property governing the thermomechanical behaviour of nuclear fuels. It depends, in particular, on their microstructure and porosity, which can reach high levels in MOX fuels (mixed uranium and plutonium oxides) used in fast neutron reactors (FNRs).
This PhD aims to understand and quantify the influence of porosity on the thermal conductivity of MOX fuels, with the objective of establishing a predictive law that accounts for pore fraction, size, shape and interconnectivity. The study will build on recent experimental measurements performed at the European Commission’s Joint Research Centre (JRC) in Karlsruhe, at temperatures of up to 2700K, and on comparisons between these measurements and numerical simulations at the microstructural scale.
The PhD will be carried out at CEA Cadarache, within IRESNE, in the Laboratory for Expertise and Validation of Multi-Reactor-Technology Applications (LEVA). The collaboration with the JRC will provide an international scientific environment. The research will lead to scientific publications and conference presentations. It will also provide the opportunity to develop skills in experimental data analysis and numerical modelling, which are transferable to other areas of materials science and engineering.
Thermal conductivity of mixed oxide fuel (MOX): effect of temperature, elementary chemical composition, microstructure and burn-up in reactor, PhD thesis by Plamen Bonev at CEA, DEC/SESC/LEVA (2023), available through the TEL – Thèses en ligne portal.
ENGINEERING SUPERCONDUCTING SURFACES FOR PARTICLE ACCELERATORS AND QUANTUM DEVICES
This project will establish atomic layer deposition (ALD) as a versatile surface engineering approach for superconducting resonators, addressing two complementary objectives for particle accelerators and quantum technologies.
The first objective is controlled doping of bulk niobium superconducting radiofrequency (SRF) cavities. Conformal films up to 10 nm thick will provide solid dopant sources, followed by high-vacuum annealing to tailor dopant profiles within the RF-active region. This strategy aims to improve uniformity, explore alternative dopants and eliminate subsequent electropolishing. Particular attention will focus on suppressing hydride formation while preserving the superconducting energy gap. Target performances include quality factors of 8 × 10¹° at 2 K and accelerating gradients of 40–45 MV/m. Supported by the European EPITA project, this work addresses requirements for future accelerators, including the Future Circular Collider.
The second objective is developing protective coatings with minimal dielectric losses by mitigating defects, particularly two-level systems. Building on CEA treatments combining dielectric oxide coatings and annealing that achieved record quantum-regime quality factors, alternative compounds will be investigated.
Ultimately, one treatment could enhance performance across quantum and accelerator operating regimes. Tunneling spectroscopy, X-ray spectroscopy and TOF-SIMS will correlate local superconducting properties with surface chemistry and dopant distributions. Two ALD systems will support development on coupons and transfer to resonators, building on demonstrated coating uniformity inside cavities. RF measurements will be conducted at CEA and partner laboratories, with Fermilab evaluating promising treatments at millikelvin temperatures. These investigations will clarify performance-limiting mechanisms and guide the rational design of optimized superconducting surfaces for both application domains.
In-depth understanding of the dedensification phenomenon during MOX sintering and during a pseudo-thermal stability test
MOX (mixed uranium and plutonium oxide) is a civil nuclear fuel. It comes in the form of cylindrical pellets with a diameter and height of approximately 10 mm. It is produced using a process that includes a high-temperature sintering step in a controlled atmosphere. During the sintering of MOX fuel pellets—formed via uniaxial pressing from a previously prepared granular feedstock—a phenomenon of dedensification resulting in swelling may occur under certain conditions. This swelling can persist during thermal stability tests intended to simulate specific in-reactor irradiation conditions.
The aim of this thesis is to identify the physicochemical mechanisms underlying this dedensification phenomenon and to formulate them mathematically in order to develop a model.
The thesis therefore includes an experimental component—to be conducted at Cadarache (using UO2) and Marcoule (using MOX)—to provide data for the model, which will be mathematically formulated during a theoretical phase carried out in collaboration with modelers at Cadarache.
Accelerated design of High Entropy Oxide thin films for carbon-free energies
Over the past decade, high-entropy oxides have emerged as a promising new class of materials for energy-related applications and corrosion resistance in extreme environments, both of which are central areas of CEA's activities.
However, their optimization remains limited by the vast compositional space that needs to be explored, leading most studies to focus on equimolar compositions.
This PhD project aims to overcome this limitation by combining combinatorial PVD using HiPIMS technology with artificial intelligence approaches based on Bayesian optimization, an approach that has not yet been explored for these materials.
The objective is to master the deposition of high-entropy oxides and optimize their properties for corrosion protection in high-temperature electrolyzers dedicated to low-carbon hydrogen production.
The deposited coatings will exhibit controlled stoichiometry, structure, microstructure, and texture, and will be characterized both in situ and ex situ using complementary techniques.
The project will provide a proof of concept for coupling combinatorial PVD with Bayesian optimization to accelerate the discovery and optimization of new materials.
This PhD project is part of the international doctoral programme of PEPR DIADEM and will bring together CEA (Saclay), ESRF (Grenoble), and NIMS (Japan).