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The recently published DeePMD model(https://github.com/deepmodeling/deepmd-kit), based on a deep neural networkarchitecture, brings the hope of solving the time-scale issue which oftenprevents the application of first principle molecular dynamics to physicalsystems. With this contribution we assess the performance of the DeePMDpotential on a real-life application and model diffusion of ions in solid-stateelectrolytes. We consider as test cases the well known Li10GeP2S12,Li7La3Zr2O12 and Na3Zr2Si2PO12. We develop and test a training protocolsuitable for the computation of diffusion coefficients, which is one of the keyproperties to be optimized for battery applications, and we find good agreementwith previous computations. Our results show that the DeePMD model may be asuccessful component of a framework to identify novel solid-state electrolytes.

Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme