Supplementary material for "Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials"

DOI

Python code for the sampling of amorphous thiphosphate-type solid-state electrolytes with arbitrary stoichiometry. The code has been used in the paper "Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials" (C. Staacke, T. Huss, J. T. Margraf , K. Reuter and C. Scheurer, Nanomaterials 2022, 12(17), 2950; https://doi.org/10.3390/nano12172950) for the sampling of different amorphous cells of the LPS family. Furthermore the Gaussian Approximation Potential (GAP) derived in this work and the corresponding training data are provided.

Identifier
DOI https://doi.org/10.17617/3.YKEMMO
Metadata Access https://edmond.mpg.de/api/datasets/export?exporter=dataverse_json&persistentId=doi:10.17617/3.YKEMMO
Provenance
Creator Huss, Tabea
Publisher Edmond
Publication Year 2026
OpenAccess true
Contact huss(at)fhi-berlin.mpg.de
Representation
Language English
Resource Type Dataset
Version 1
Discipline Other