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

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

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 ha...

Full description

Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Portfolio 2023-01-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-022-00951-z

Internet

https://doi.org/10.1038/s41524-022-00951-z

Similar Items