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

Aris Marcolongo1*, Tobias Binninger1, Federico Zipoli1, Teodoro Laino1

1 Cognitive Computing and Computational Sciences Department, IBM Research – Zurich, Saumerstrasse 4, CH-8803 Ruschlikon, Switzerland

* Corresponding authors emails:
DOI10.24435/materialscloud:2019.0067/v1 [version v1]

Publication date: Oct 25, 2019

How to cite this record

Aris Marcolongo, Tobias Binninger, Federico Zipoli, Teodoro Laino, Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training scheme, Materials Cloud Archive 2019.0067/v1 (2019), doi: 10.24435/materialscloud:2019.0067/v1.


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 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.

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File name Size Description
2.3 KiB
45.7 MiB Configurations used for training of the different materials and data to reproduce the plots.


Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0.

External references

Preprint (Preprint where the data is discussed)
A. Marcolongo, T. Binninger, F. Zipoli, T.Laino, arXiv:1910.10090


MARVEL/Inc1 Neural-network potentials Solid-state electrolytes

Version history:

2019.0067/v1 (version v1) [This version] Oct 25, 2019 DOI10.24435/materialscloud:2019.0067/v1