Machine learning approach to the extended Hubbard model

Caleca, Filippo (2022) Machine learning approach to the extended Hubbard model. [Laurea magistrale], Università di Bologna, Corso di Studio in Physics [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Creative Commons: Attribuzione - Non commerciale - Non opere derivate 4.0 (CC BY-NC-ND 4.0)

Download (4MB)

Abstract

The 1d extended Hubbard model with soft-shoulder potential has proved itself to be very difficult to study due its non solvability and to competition between terms of the Hamiltonian. Given this, we tried to investigate its phase diagram for filling n=2/5 and range of soft-shoulder potential r=2 by using Machine Learning techniques. That led to a rich phase diagram; calling U, V the parameters associated to the Hubbard potential and the soft-shoulder potential respectively, we found that for V<5 and U>3 the system is always in Tomonaga Luttinger Liquid phase, then becomes a Cluster Luttinger Liquid for 5<V<7 (with different block structure depending on the relative values of U and V), and finally undergoes a general crystallization or V>7, with a quasi-perfect crystal in the U<3V/2 and U>5 region. Finally we found that for U<5 and V>2-3 the system shall maintain the Cluster Luttinger Liquid structure, with a residual in-block single particle mobility.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Caleca, Filippo
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
THEORETICAL PHYSICS
Ordinamento Cds
DM270
Parole chiave
Hubbard model,quantum many body,Machine Learning
Data di discussione della Tesi
23 Settembre 2022
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento

^