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

TL;DR

This paper introduces a machine-learning based computational method to accurately simulate Raman spectra of titanium carbide MXene, accounting for temperature, surface disorder, and composition effects, improving interpretation of experimental spectra.

Contribution

The work develops a novel simulation approach combining machine-learning force fields with Raman tensor reconstruction for complex 2D materials.

Findings

Simulated spectra match experimental data when including temperature and disorder effects.

Surface composition influences Raman peak positions and intensities.

The method improves interpretation of MXene surface chemistry from Raman spectra.

Abstract

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.