A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials.

Abstract

The rate performance of any electrode or solid electrolyte material used in a battery is critically dependent on the migration barrier (E_m) governing the motion of the intercalant ion, which is a difficult-to-estimate quantity both experimentally and computationally. The foundation for constructing and validating accurate machine learning (ML) models that are capable of predicting E_m, and hence accelerating the discovery of novel electrodes and solid electrolytes, lies in the availability of high-quality dataset(s) containing E_m. Addressing this critical requirement, we present a comprehensive dataset comprising 621 distinct literature-reported E_m values calculated using density functional theory based nudged elastic band computations, across 443 compositions and 27 structural groups consisting of various compounds that have been explored as electrodes or solid electrolytes in batteries. Our dataset includes compositions corresponding to fully charged and/or discharged states of electrodes, with intermediate compositions incorporated in select instances. Crucially, for each compound, our dataset provides structural information, including the initial and final positions of the migrating ion, along with its corresponding E_m in easy-to-use .xlsx and JSON formats. We envision our dataset to be highly useful for the scientific community, facilitating the development of advanced ML models that can predict E_m precisely and accelerate materials discovery.