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While Bernal stacked bilayer graphene bears two distinct atom types in itslattice, there exists no analytical framework addressing the number of atomicenvironments that emerge in twisted bilayer graphene superlattices. In thiswork, we have computationally analyzed 120 different twisted bilayersuperlattices using descriptor functions to study the emergent localenvironments. Our study reveals that the number of atoms with unique localenvironments depend on the superlattice size linearly. Moreover, this lineardependence manifests itself on two distinct lines and this automaticallysuggests a new classification scheme based on the local environments. As apossible application, the use of local environments in the investigation ofvibrational properties is discussed with respect to the existing literature.Molecular dynamics simulations are performed to calculate the phonon density ofstates of the 120 structures as well as the local phonon density of states oftheir individual atoms. The similarity of the contributions of local density ofstates coming from atoms with the same local environment is demonstrated. Localdensity of states of the atoms with unique local environments of an arbitraryselection of the structures is then used to train a machine learning model.This model is used to predict the phonon spectra of twisted bilayer structures.Performance of the trained model is discussed thoroughly via differentselection of training and test sets, and it is shown that the model proveseffective in predicting the vibrational properties of any given twisted bilayerstructure. The possible applications of the generic method presented whichreaches far beyond twisted bilayer graphene is also discussed.

Emergent Atomic Environments in Twisted Bilayer Graphene and Their Use in the Prediction of the Vibrational Properties