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We study the characteristic probability density distribution of random flatband models by machine learning. The models considered here are constructed onthe basis of the molecular-orbital representation, which guarantees theexistence of the macroscopically degenerate zero-energy modes even in thepresence of randomness. We find that flat band states are successfullydistinguished from conventional extended and localized states, indicating thecharacteristic feature of the flat band states. We also find that the flat bandstates can be detected when the target data are defined in the differentlattice from the training data, which implies the universal feature of the flatband states constructed by the molecular-orbital representation.

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