A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
Abstrak
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.
Topik & Kata Kunci
Penulis (32)
Cameron J. Hargreaves
Michael W. Gaultois
Luke M. Daniels
Emma J. Watts
Vitaliy A. Kurlin
Michael Moran
Yun Dang
Rhun Morris
Alexandra Morscher
Kate Thompson
Matthew A. Wright
Beluvalli-Eshwarappa Prasad
Frédéric Blanc
Chris M. Collins
Catriona A. Crawford
Benjamin B. Duff
Jae Evans
Jacinthe Gamon
Guopeng Han
Bernhard T. Leube
Hongjun Niu
Arnaud J. Perez
Aris Robinson
Oliver Rogan
Paul M. Sharp
Elvis Shoko
Manel Sonni
William J. Thomas
Andrij Vasylenko
Lu Wang
Matthew J. Rosseinsky
Matthew S. Dyer
Akses Cepat
- Tahun Terbit
- 2023
- Sumber Database
- DOAJ
- DOI
- 10.1038/s41524-022-00951-z
- Akses
- Open Access ✓