Ethan Berger^1*, Zhong-Peng Lv², Hannu-Pekka Komsa^1*

1 Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 4500, Oulu, FIN-90014, Finland

2 Department of Applied Physics, Aalto University, Aalto, FIN-00076, Finland

* Corresponding authors emails: ethan.berger@oulu.fi, hannu-pekka.komsa@oulu.fi

DOI10.24435/materialscloud:w2-g5 [version v1]

Publication date: Dec 07, 2022

How to cite this record

Ethan Berger, Zhong-Peng Lv, Hannu-Pekka Komsa, Raman spectra of 2D titanium carbide MXene from machine-learning force field molecular dynamics, Materials Cloud Archive 2022.168 (2022), doi: 10.24435/materialscloud:w2-g5.

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:5ee434a99dbd71d6ef8ecb018f761e72	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

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Raman spectroscopy molecular dynamics machine learning