Aris Marcolongo^1*, 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: aris.marcolongo@gmail.com

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.

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:f7f261b0382ffbc121641e2da55f1df3	2.3 KiB
ANN_diffusion.tar.gz MD5md5:6f96927bdbb9a8b986f8b6591f44ad7d	45.7 MiB	Configurations used for training of the different materials and data to reproduce the plots.

License

Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

MARVEL/Inc1 Neural-network potentials Solid-state electrolytes