Domaines
Condensed matter
Statistical physics
Low dimension physics
Nanophysics, nanophotonics, 2D materials and van der Waals heterostructures,, surface physicss, new electronic states of matter
Type de stage
Théorique, numérique Description
The proposed thesis focuses on the implementation of the nested sampling method, a machine learning method issued from Bayesian statistics, for exploring complex potential energy landscapes of condensed matter systems that include nuclear quantum effects. As a benchmark test system, Lennard-Jones clusters in 2D will be considered. The theoretical findings will directly be compared to the recent experimental measurements of noble gas clusters confined in a graphene sandwich. The nuclear quantum effects are evaluated using the Feynman path integral formalism.
Contact
Martino Trassinelli