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Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models
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  • Published: 01 April 2026

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

  • Seong-Hoon Jang1,
  • Shin Kiyohara1,
  • Hitoshi Takamura2 &
  • …
  • Yu Kumagai1,3 

Scientific Data , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

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 remains 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 (Ea) 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, we replotted these using the correct one. To illustrate how the database can be used, we constructed interpretable regression models for predicting oxygen ionic conductivity. Two symbolic regression models for Ea 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.

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Data availability

The total dataset created in this study is openly available at https://github.com/JerryGarcia1995/OxygenIonConductor/blob/main/database/oxygen_ion_conductor_dataset.csv and https://doi.org/10.5281/zenodo.18947543.

Code availability

The source code supporting materials prediction and design in this study is openly available at https://github.com/JerryGarcia1995/OxygenIonConductor/tree/main/modelling_GoodRegressor.

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Acknowledgements

This work has been supported by JST FOREST Program (JPMJFR235S). Parts of the numerical calculations have been done using the facilities of the Supercomputer Center, the Institute for Solid State Physics, the University of Tokyo.

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Authors and Affiliations

  1. Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan

    Seong-Hoon Jang, Shin Kiyohara & Yu Kumagai

  2. Department of Materials Science, Graduate School of Engineering, Tohoku University, 6-6-02 Aramaki, Aoba-ku, Sendai, 980-8579, Japan

    Hitoshi Takamura

  3. Organization for Advanced Studies, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan

    Yu Kumagai

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Contributions

Y.K. proposed the project. S.-H.J. curated all data under the supervision of H.T., developed the regression model, and prepared the initial draft of the manuscript. The manuscript was revised by S.-H.J. and Y.K. All authors contributed to discussions and reviewed the manuscript.

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Correspondence to Seong-Hoon Jang or Yu Kumagai.

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Jang, SH., Kiyohara, S., Takamura, H. et al. Charting the Landscape of Oxygen Ion Conductors: A 60-Year Dataset with Interpretable Regression Models. Sci Data (2026). https://doi.org/10.1038/s41597-026-07100-x

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  • Received: 23 January 2026

  • Accepted: 19 March 2026

  • Published: 01 April 2026

  • DOI: https://doi.org/10.1038/s41597-026-07100-x

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