Seed 12

New topological materials: Insights from machine learning and strong correlations

Principal Investigator
Nicolas Regnault, Visiting Research Scholar

Seed start and end dates: November 1, 2019 - October 31, 2020

We propose to predict and study the new generation of topological materials.

  1. Using machine learning techniques, we will develop tools to quickly characterize the topological properties of materials from their crystalline structure and predict new ones.
  2. Van der Waals heterostructures have emerged as a platform for discovering exotic states of matter and engineering new functionalities. We will study the emergence of many-body “high-temperature” quantum states of matter in the flat bands of these Moiré lattices. We will propose new Moiré lattices based on magnetic materials and transition metal dichalcogenides to engineer new or more robust many-body phases.
  3. Using the combination of tensor-network methods, large scale exact diagonalization, and quantum information techniques, we will investigate the possible emergence of the new strongly correlated phases in higher order topological insulators.