  MXenes represent one of the largest class of 2D materials with promising
applications in many fields and their properties tunable by the surface group
composition. Raman spectroscopy is expected to yield rich information about the
surface composition, but the interpretation of measured spectra has proven
challenging. The interpretation is usually done via comparison to simulated
spectra, but there are large discrepancies between the experimental and earlier
simulated spectra. In this work, we develop a computational approach to
simulate Raman spectra of complex materials that combines machine-learning
force-field molecular dynamics and reconstruction of Raman tensors via
projection to pristine system modes. The approach can account for the effects
of finite temperature, mixed surfaces, and disorder. We apply our approach to
simulate Raman spectra of titanium carbide MXene and show that all these
effects must be included in order to properly reproduce the experimental
spectra, in particular the broad features. We discuss the origin of the peaks
and how they evolve with surface composition, which can then be used to
interpret experimental results.
