'Twistronics' in bidimensional materials

• Grant number: 23/09394-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2023
• End date: October 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Felipe David Crasto de Lima
• Grantee: Marcos de Cerqueira Leite Pimentel
• Host Institution: Centro Nacional de Pesquisa em Energia e Materiais (CNPEM). Ministério da Ciência, Tecnologia e Inovação (Brasil). Campinas , SP, Brazil

Abstract

Recently, the discovery of unconventional superconductivity in graphene bilayers has highlighted the possibility of understanding both the basic physics and applications in low-dimensional systems. This phenomenon occurs when one layer is rotated with respect to the other by a specific angle, known as the 'magic angle'. From a materials simulation perspective, studying moiré patterns requires a large number of atoms to reproduce the spatial scale of the pattern. This computationally inefficiency hinders the direct use of first-principles calculations for a general moiré pattern. Currently, the exploration of these patterns follows two main approaches: (i) continuous models and (ii) localized discrete models using tight-binding methods. In both cases, the system depends on parameters to capture different moiré pattern-dependent phenomena, such as strain fields, layer spacing variations, charge puddles, etc. In this project, we will address how to find such parameters from a tight-binding method calculation in order to capture the behavior of first-principles calculations. By extracting the parameters from first-principles calculations on smaller systems, we will extrapolate to larger systems. Since these parameters exhibit spatial variation dependent on the structure, we will employ machine learning approaches to correlate the local structure with the coupling between layers. This approach will be crucial for capturing realistic effects in tight-binding model systems.