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Machine learning the microscopic form of nematic order in twisted double-bilayer graphene

Physics

Machine learning the microscopic form of nematic order in twisted double-bilayer graphene

J. A. Sobral, S. Obernauer, et al.

Explore the groundbreaking use of convolutional neural networks to uncover the intricate electronic nematic order in twisted double-bilayer graphene. This innovative research by João Augusto Sobral, Stefan Obernauer, Simon Turkel, Abhay N. Pasupathy, and Mathias S. Scheurer reveals how microscopic details of correlated phenomena can be deciphered from high-dimensional STM data.

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Playback language: English
Abstract
This work demonstrates the use of convolutional neural networks (CNNs) to learn effective theoretical models from scanning tunneling microscopy (STM) data on correlated moiré superlattices, specifically focusing on electronic nematic order in twisted double-bilayer graphene (TDBG). The large moiré unit cells and tunability of these systems allow for the collection of high-dimensional datasets. The CNNs learn the microscopic nematic order parameter by incorporating correlations between the local density of states (LDOS) at different energies, distinguishing it from heterostrain. This methodology proves powerful for investigating microscopic details of correlated phenomena in moiré systems.
Publisher
Nature Communications
Published On
Aug 17, 2023
Authors
João Augusto Sobral, Stefan Obernauer, Simon Turkel, Abhay N. Pasupathy, Mathias S. Scheurer
Tags
convolutional neural networks
scanning tunneling microscopy
electronic nematic order
twisted double-bilayer graphene
correlated phenomena
moiré superlattices
local density of states
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