Machine Learning Study on the Flat-Band States Constructed by Molecular-Orbital Representation with Randomness

We study the characteristic probability density distribution of random flat-band models by machine learning. The models considered here are constructed on the basis of the molecular-orbital representation, which guarantees the existence of macroscopically degenerate zero-energy modes even in the pre...

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Bibliographic Details
Published in:Journal of the Physical Society of Japan 2022-04, Vol.91 (4), p.044703
Main Authors: Kuroda, Takumi, Mizoguchi, Tomonari, Araki, Hiromu, Hatsugai, Yasuhiro
Format: Article
Language:English
Subjects:
Density distribution
Lattice vibration
Machine learning
Randomness
Representations
Target detection
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Summary:We study the characteristic probability density distribution of random flat-band models by machine learning. The models considered here are constructed on the basis of the molecular-orbital representation, which guarantees the existence of macroscopically degenerate zero-energy modes even in the presence of randomness. We find that flat-band states are successfully distinguished from conventional extended and localized states, indicating the characteristic feature of the flat-band states. We also find that the flat-band states can be detected when the target data are defined in a different lattice from the training data, which implies the universal feature of the flat-band states constructed by the molecular-orbital representation.
ISSN:0031-9015
1347-4073
DOI:10.7566/JPSJ.91.044703