A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
نویسندگان
چکیده
Abstract The application of machine learning models to predict material properties is determined by the availability high-quality data. We present an expert-curated dataset lithium ion conductors and associated conductivities measured a.c. impedance spectroscopy. This has 820 entries collected from 214 sources; contain a chemical composition, expert-assigned structural label, ionic conductivity at specific temperature (from 5 873 °C). There are 403 unique compositions with near room (15–35 materials contained in this placed context compounds reported Inorganic Crystal Structure Database unsupervised Element Movers Distance. used train CrabNet-based classifier estimate whether composition high or low conductivity. practical tool aid experimentalists prioritizing candidates for further investigation as conductors.
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ژورنال
عنوان ژورنال: npj computational materials
سال: 2023
ISSN: ['2057-3960']
DOI: https://doi.org/10.1038/s41524-022-00951-z