Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 the 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 the different
lattice from the training data, which implies the universal feature of the flat
band states constructed by the molecular-orbital representation.

9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math