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).