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MXenes represent one of the largest class of 2D materials with promisingapplications in many fields and their properties tunable by the surface groupcomposition. Raman spectroscopy is expected to yield rich information about thesurface composition, but the interpretation of measured spectra has provenchallenging. The interpretation is usually done via comparison to simulatedspectra, but there are large discrepancies between the experimental and earliersimulated spectra. In this work, we develop a computational approach tosimulate Raman spectra of complex materials that combines machine-learningforce-field molecular dynamics and reconstruction of Raman tensors viaprojection to pristine system modes. The approach can account for the effectsof finite temperature, mixed surfaces, and disorder. We apply our approach tosimulate Raman spectra of titanium carbide MXene and show that all theseeffects must be included in order to properly reproduce the experimentalspectra, in particular the broad features. We discuss the origin of the peaksand how they evolve with surface composition, which can then be used tointerpret experimental results.

Raman Spectra of Titanium Carbide MXene from Machine-Learning Force Field Molecular Dynamics