1. A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
- Author
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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, and Matthew S. Dyer
- Subjects
Mechanics of Materials ,Modeling and Simulation ,General Materials Science ,Computer Science Applications - 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.
- Published
- 2023
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