We perform theoretical research in Condensed Matter Physics. We use and develop a number of theoretical tools ranging from model Hamiltonians to advanced computational methods. Our research involves important analytical as well as numerical developments.
Quantum materials
– Magnetically and electronically frustrated systems
– Multiferroics and magnetoelectric materials
– Superconductivity and superconducting materials
– Topological matter
First principles theory and simulations
– DFT and TDDFT
– Many-body perturbation theory (GW approximation and the Bethe-Salpeter equation)
– Wavefunction-based complete active space configuration interaction (CASCI)
Position type: Funded theses
Contact: LEPETIT Marie-Bernadette - +33 4 76 88 90 45 | -
In order to predict new magnetic materials with desired properties one needs to be able to scan large number of systems and if possible the whole set of possibilities in a given family of materials. To reach this goal one need to evaluate on-the-fly the magnetic interactions of any given system. The accurate calculation of the latter however presently requires time-consuming procedures. One thus need to change the methodology and use a non-heuristic approach such as machine learning methods. The low computational cost of these new “in silico” methods offers a way to meet the challenge of an “automated” exploration of the field of possibilities.
In the field of magnetic materials, the use of deep learning methods is however quite uncommon and essentially focused on the determination of transition temperature, or phase diagrams and not on the determination of the magnetic interactions.
Research topic and facilities available : In this project, we propose to explore this new field by elaborating a machine learning methodology to predict the magnetic properties of metal-organic-frameworks (MOF). The project will explore the best type of deep learning method to be used, the structural or electronic descriptors needed to predict the magnetic interactions, the construction of the training data sets.
This work will be done on French supercomputer facilities. The supervisor will provide the computer hours allocation.
Person in charge: Andrés CANO
Permanents
Jean-Pierre JULIEN
Personnel Chercheur - UGA
Jean-Pierre.Julien@neel.cnrs.fr
Phone: 04 76 88 78 99
Office: D-302
Marie-Bernadette LEPETIT
Personnel Chercheur - CNRS
marie-bernadette.lepetit@neel.cnrs.fr
Phone: 04 76 88 90 45
Office: K-211
David AMBLARD LATIL
Personnel Chercheur - UGA
Phone: 04 76 88 10 22
Office: D-216
Referent: Xavier BLASE
Jean-Baptiste DE-VAULX
Personnel Chercheur - UGA
jean-baptiste.de-vaulx@neel.cnrs.fr
Phone: 04 76 88 79 14
Office: K-207
Referent: Andres CANO
Brice KENGNI-ZANGUIM
Personnel Chercheur - CNRS
brice.kengni-zanguim@neel.cnrs.fr
Phone: 04 76 88 90 45
Office: K-211
Referent: Marie-Bernadette LEPETIT
Sergi MAS-MENDOZA
Personnel Chercheur - CNRS
sergi.mas-mendoza@neel.cnrs.fr
Office: M-209
Referent: Adolfo GRUSHIN
Alexandre PERRIN
Personnel Chercheur - UGA
Phone: 04 76 88 78 29
Office: M-214
Referent: Jean-Pierre JULIEN
Mauricio RODRIGUEZ-MAYORGA
Personnel Chercheur - CNRS
mauricio.rodriguez-mayorga@neel.cnrs.fr
Phone: 04 76 88 79 19
Office: K-200
Referent: Xavier BLASE
Lucien SALMON
Personnel Chercheur - UGA
Office: D-314
Referent: Benjamin CANALS