A researcher at ICMPE for 15 years, I left the Institute in August 2023 to join the international unit IRL3629: Laboratory for Innovative Key Materials and Structures, LINK. This unit, managed by 3 supervisory bodies (CNRS – Saint Gobain – NIMS), is located in Tsukuba, at the heart of innovation in Japanese materials science. ICMPE is a partner unit of LINK, with which close collaborations are maintained.
- Electronic band structure from DFT calculations
- Lattice vibration contribution by phonon calculation
- Disordered solution treated by a cluster expansion formalism (SQS, CEM, CVM)
- High throughput calculations, combinatorial approaches
- Supervised machine learning
I am interested in the stability of solid phases and their electronic structure determined by density functional theory (DFT) calculations. In particular, I study the modification of physicochemical properties (nature of bonds, magnetism, structural ordering, reaction mechanism) during a transformation, such as the hydrogen absorption in intermetallic compounds. To support this research, it is important to me to develop various complementary methodologies.
By the coupling of the DFT method with thermodynamic modeling like the Calphad method, I am particularly interested in the prediction of phase equilibrium in multi-component systems. I am also studying the vibrational properties of the crystal lattice through phonon calculations (harmonics and quasi-harmonics) to better understand the phase mechanical stability and to determine thermodynamic properties at finite temperature. Not limited to only ordered phases, I apply techniques such as cluster expansion theory to deal with a disordered solution by SQS (Special Quasirandom Structure) or CEM (Cluster Expansion Energy).
More recently, I have also been interested in the development of two new emerging approaches for the prediction of new innovative and efficient materials in specific applications (thermoelectricity, energy storage):
- the massive DFT calculation for the screening of intermetallic compounds (high throughput)
- supervising machine learning (classical regression or generative algorithms)
TEACHING AND TUTORING
- Electronic structure calculation with DFT (course, pratical works, Master UPEC)
- Thermodynamic modelling (pratical works, Master UPEC)
- Prediction in materials science with machine learning (course, pratical works)
- Former head of MC group of M2i department at ICMPE
- Co-director of the GDR IAMAT (Artificial Intelligence in Materials Science)