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Unsupervised learning of two-component nematicity from STM data on magic angle bilayer graphene

Taranto, William and Lederer, Samuel and Choi, Youngjoon and Izmailov, Pavel and Wilson, Andrew Gordon and Nadj-Perge, Stevan and Kim, Eun-Ah (2022) Unsupervised learning of two-component nematicity from STM data on magic angle bilayer graphene. . (Unpublished)

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Moiré materials such as magic angle twisted bilayer graphene (MATBG) exhibit remarkable phenomenology, but present significant challenges for certain experimental methods, particularly scanning probes such as scanning tunneling microscopy (STM). Typical STM studies that can image tens of thousands of atomic unit cells can image roughly ten moiré cells, making data analysis statistically fraught. Here, we propose a method to mitigate this problem by aggregating STM conductance data from several bias voltages, and then using the unsupervised machine learning method of gaussian mixture model clustering to draw maximal insight from the resulting dataset. We apply this method, using as input coarse-grained bond variables respecting the point group symmetry, to investigate nematic ordering tendencies in MATBG for both charge neutral and hole-doped samples. For the charge-neutral dataset, the clustering reveals the surprising coexistence of multiple types of nematicity that are unrelated by symmetry, and therefore generically nondegenerate. By contrast, the clustering in the hole doped data is consistent with long range order of a single type. Beyond its value in analyzing nematicity in MATBG, our method has the potential to enhance understanding of symmetry breaking and its spatial variation in a variety of moiré materials.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Lederer, Samuel0000-0002-7443-3859
Wilson, Andrew Gordon0000-0002-2011-3315
Nadj-Perge, Stevan0000-0002-2916-360X
Additional Information:SL, PI , AGW, and E-AK acknowledge NSF, Institutes for Data-Intensive Research in Science and Engineering – Frameworks (OAC-19347141934714). SL is supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator (QSA). S.N-P. acknowledges support from the NSF (grant DMR-2005129) and the Sloan Foundation (grant FG-2020-13716).
Group:Institute for Quantum Information and Matter
Funding AgencyGrant Number
Department of Energy (DOE)UNSPECIFIED
Alfred P. Sloan FoundationFG-2020-13716
Record Number:CaltechAUTHORS:20220524-180254587
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114893
Deposited By: George Porter
Deposited On:31 May 2022 19:21
Last Modified:31 May 2022 19:21

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