  Twisted bilayer graphene (TBLG) has emerged as an exciting new material with
tunable electronic properties ranging from superconductivity to correlated
insulating phases. But current methods of fabrication and identification of
TBLG are painstaking and laborious. In this work, we combine Raman spectroscopy
with the Gaussian mixture model (GMM) data clustering algorithm to identify
areas with particular twist angles, from a TBLG sample with a mixture of
orientations. We present two approaches to this cluster analysis: training the
GMM on Raman parameters returned by peak fits, and on full Raman spectra with
dimensionality reduced by principal component analysis. In both cases we
demonstrate that GMM can identify regions of distinct twist angle from within
Raman datacubes. We also show that once a model has been trained, and the
identified clusters labelled, the model can be reapplied to new Raman 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
computerised detection of particular twist angles from automated large-area
scans.
