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

Aris Marcolongo, Tobias Binninger, Federico Zipoli & Teodoro Laino
The recently published DeePMD model, 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...
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