Domaines
Condensed matter
Statistical physics
Low dimension physics
Type of internship
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