Data cluster analysis and machine learning for classification of twisted bilayer graphene

Twisted bilayer graphene (TBLG) has emerged as an exciting new material with tunable electronic properties, but current methods of fabrication and identification of TBLG are painstaking and laborious. We combine Raman spectroscopy with Gaussian mixture model (GMM) data clustering to identify areas with particular twist angles, from a TBLG sample with a mixture of orientations. We present two approaches: training the GMM on Raman parameters returned by peak fits, and on full spectra with dimensionality reduced by principal component analysis. In both cases, GMM identifies regions of distinct twist angle from within Raman datacubes. We also show that once a model has been trained, and the identified clusters labelled, it can be reapplied to new scans to assess the similarity between the materials in the new region and the testing region. This could enable high-throughput fabrication of TBLG, by allowing computerized detection of particular twist angles from automated large-area scans.