Deep learning approach for quantum many-body physics

• Grant number: 20/10505-6
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2021
• End date: August 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Silvio Antonio Sachetto Vitiello
• Grantee: William Freitas e Silva
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 16/17612-7 - Dynamics of many-body systems IV, AP.TEM

Abstract

This project aims to study systems of current interest such as fermionic helium and warm dense matter. For instance, properties as susceptibility and stability of the unpolarised state of 3He are among the topics of study, as well as the behaviour of electron gas under warm dense conditions. To investigate the problem, the goal is to develop a variational wave-function and a variational density matrix through artificial neural networks by combining both "deep learning" and "iterative backflow" approaches. The proposed work allows the research of important ongoing topics in the field of Computational Physics. Moreover, there are possibly promising outcomes worthwhile of looking into it. (AU)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SILVA, William Freitas e. Deep learning approach for quantum many-body systems. 2024. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Instituto de Física Gleb Wataghin Campinas, SP.