Raman spectra of 2D titanium carbide MXene from machine-learning force field molecular dynamics

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