Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models

Abstract

Oxygen ion conductors are indispensable materials for such as solid oxide fuel cells, sensors, and membranes. Despite extensive research across diverse structural families, systematic data enabling comparative analysis remain scarce. Here, we present a curated dataset of oxygen ion conductors compiled from $84$ experimental reports spanning $60$ years, covering $483$ materials. Each record includes activation energy ($E_a$) and prefactor ($A$) derived from Arrhenius plots, alongside detailed metadata on structure, composition, measurement method, and data source. When the original papers derive these using an erroneous Arrhenius equation $\sigma_T=A\exp{\left(-\frac{E_a}{RT}\right)}$, where ($\sigma_T$ is the oxygen ion conductivity at temperature $T$ and $R$ is the gas constant), we replotted these using the correct one, $\sigma_{T}T=A\exp{\left(-\frac{E_a}{RT}\right)}$. To illustrate how the database can be used, we constructed interpretable regression models for predicting oxygen ionic conductivity. Two symbolic regression models for E_a and A suggest that oxygen ion transport is primarily governed by local coordination environment and the electrostatic interactions, respectively. This dataset establishes a reliable foundation for data-driven discovery and predictive modeling of next-generation oxygen ion conductors.