Title:	Novel two dimensional materials: magnetism, transport and topology
DNr:	NAISS 2025/3-68
Project Type:	NAISS Large
Principal Investigator:	Biplab Sanyal <biplab.sanyal@physics.uu.se>
Affiliation:	Uppsala universitet
Duration:	2026-01-01 – 2026-07-01
Classification:	10304 10407
Homepage:	https://katalog.uu.se/profile/?id=N1-83
Keywords:

Abstract

In this proposal, we intend to study novel 2D quantum systems relevant to future robust quantum technologies. This is of great importance for developing quantum computers, which will facilitate the simulation of large quantum systems exponentially faster than a classical computing machine and spintronic applications, where spin degrees of freedom of electrons are utilized for realizing fast and efficient information storage and processing. For these purposes, one needs first principles materials-specific theory to explore possible stable configurations of novel two-dimensional structures. First, structures of novel 2D materials will be studied by evolution-based structure prediction algorithms coupled with ab initio density functional theory (DFT) followed by the scrutiny of their stability using phonon calculations and molecular dynamics simulations. Furthermore, ab initio electronic structure calculations will be performed followed by constructing low-energy Hamiltonians using Wannierization from ab initio data. This will lead to the characterization of topological properties. 2D magnets will be explored for an in-depth understanding of their magnetic interactions along with the proximity effects in van der Waals heterostructures. In this regard, large scale simulations will be performed using finite element method coupled with DFT to study non-collinear magnetism in twisted bilayers of 2D magnets. Finally, quantum charge and spin transport calculations including the effects of electron-phonon coupling will be performed with tight-binding Hamiltonian with the parameters obtained from ab initio calculations. Our python-based automated high-throughput platform will enable us to generate big datasets for machine learning models. Several state-of-the-art codes (commercial and public domain) will be used for this project.