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##### Neural network force field model of Mo_{x}W_{1-x}S_{2} alloys for mechanical and
thermal properties

**Authors**: Martin Keller

**Ref.**: Master thesis, Martin-Luther University of Halle-Wittenberg (2021)

**Abstract**: A deep neural network was trained to predict the atomic forces of Mo_{x}W_{1-x}S_{2} alloys.
For this a dataset of about 2000 DFT calculations of 3x3 to 5x5 Mo_{x}W_{1-x}S_{2} supercells
with atomic displacements was created. The neural network was used to compute the
phonon dispersion relations of large supercells (up to 16x16) in the second harmonic
approximation. Using a model based on the polarizabilities of the individual bonds in
a cell Raman spectra of the alloys were predicted from the phonon eigenmodes at the
Γ point. The calculations were performed on so-called special quasi-random supercells.
Some analysis of the behaviour of the A_{1} mode during the change of composition from
MoS_{2} to WS_{2} is conducted. A splitting of the mode at 50% concentration into 4 subpeaks
is observed in agreement with experiments. Correlation between the subpeaks and the
different environments of the S atoms is shown. Further the discrepancy between the
structures usually used for alloy calculations and real systems is investigated. Additionally the neural network was used to compute the thermal conductivity of the alloys using
a linearized phonon Boltzmann equation and the relaxation time approximation. The
results are only qualitative information due to the small size of the cells used (5x5).