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

TL;DR

This paper introduces a comprehensive, high-quality dataset of migration barriers ($E_m$) for battery materials, derived from literature and DFT calculations, to aid machine learning models in predicting ionic transport properties.

Contribution

The authors provide the first extensive dataset of $E_m$ values with structural details for battery materials, enabling improved ML predictions and accelerated materials discovery.

Findings

Dataset includes 619 $E_m$ values across 443 compositions.

Provides structural information and migration pathways.

Facilitates development of ML models for ionic transport prediction.

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 619 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 that correspond to fully charged and/or discharged states of electrode materials, 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 a highly useful resource for the scientific community, facilitating the development of advanced ML models that can predict $E_m$ precisely and accelerate materials discovery.