  The recently published DeePMD model
(https://github.com/deepmodeling/deepmd-kit), based on a deep neural network
architecture, brings the hope of solving the time-scale issue which often
prevents the application of first principle molecular dynamics to physical
systems. With this contribution we assess the performance of the DeePMD
potential on a real-life application and model diffusion of ions in solid-state
electrolytes. We consider as test cases the well known Li10GeP2S12,
Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocol
suitable for the computation of diffusion coefficients, which is one of the key
properties to be optimized for battery applications, and we find good agreement
with previous computations. Our results show that the DeePMD model may be a
successful component of a framework to identify novel solid-state electrolytes.
