Article Dans Une Revue npj Computational Materials Année : 2023

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Kate Thompson
  • Fonction : Auteur
  • PersonId : 1073285
Matthew A Wright
Jae Evans
Arnaud J Perez
Andrij Vasylenko

Résumé

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Domaines

Fichier principal
Vignette du fichier
s41524-022-00951-z.pdf (1.3 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Licence

Dates et versions

hal-04248625 , version 1 (18-10-2023)

Licence

Identifiants

Citer

Cameron J Hargreaves, Michael W Gaultois, Luke M Daniels, Emma J Watts, Vitaliy A Kurlin, et al.. A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning. npj Computational Materials, 2023, 9 (1), pp.9. ⟨10.1038/s41524-022-00951-z⟩. ⟨hal-04248625⟩
124 Consultations
269 Téléchargements

Altmetric

Partager

  • More