Publication date: Dec 07, 2022
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. This record contains input files for MLFF training and production runs, information on the training set (atomic structures, energies and forces) and some of the molecular dynamics trajectories used to obtain Raman spectra.
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File name | Size | Description |
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README.txt
MD5md5:5ee434a99dbd71d6ef8ecb018f761e72
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804 Bytes | README file containing a short description of each other file |
INCAR_Train
MD5md5:2103c11c8f1f1b25642735cef9cc6bd7
|
254 Bytes | Input file for on-the-fly training |
INCAR_Retrain
MD5md5:aa7c01dbec449b6bed81b3991def4203
|
279 Bytes | Input file for retraining the MLFF |
INCAR_Prod
MD5md5:eed5ce561f014f9295a700f68de104f4
|
236 Bytes | Input file for MD production runs |
ML_AB
MD5md5:cac2be4b7bfe97dc4e1d3e7ad14e8d45
|
26.1 MiB | Atomic structures, energies and forces of the training set |
XDATCAR_0.tar.gz
MD5md5:6617e5301bbb07529711b7bf26c199f4
|
783.0 MiB | MD trajectories for x=0 |
XDATCAR_0.25.tar.gz
MD5md5:cd7b739bb17430c3225a4694b7bf7f4a
|
741.6 MiB | MD trajectories for x=0.25 |
XDATCAR_0.5.tar.gz
MD5md5:614bb43c19bf09c7ca632e5b72004ffe
|
697.4 MiB | MD trajectories for x =0.5 |
XDATCAR_0.75.tar.gz
MD5md5:88a2af1bdb7a61550c11a9d6638f74f9
|
652.5 MiB | MD trajectories for x =0.75 |
XDATCAR_1.tar.gz
MD5md5:79bc58a61fecd07e4a7451a322a34cb0
|
604.9 MiB | MD trajectories for x=1 |
2022.168 (version v1) [This version] | Dec 07, 2022 | DOI10.24435/materialscloud:w2-g5 |