ReviewScience and technology of advanced materials2024
Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding.
Review in Science and technology of advanced materials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Recent Advances and Future Perspectives of Proton-Conducting Electrolytes for Reversible Solid Oxide Cells.Chemical reviews · 2026Article
- Toward Efficient and Reliable Chemical Upgrading Using Solid Oxide Electrochemical Reactors, Mechanisms, Challenges, and Design Principles.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Mitigating proton trapping in cubic perovskite oxides via ScONature materials · 2025Article
- Evolution and Reconstruction of Air-Electrode Surface Composition in Reversible Protonic Ceramic Cells: Mechanisms, Impacts on Catalytic Performance, and Optimization Strategies - A Review.Advanced materials (Deerfield Beach, Fla.) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review presents computational and machine learning methodologies developed during a 5-year research project on proton-conducting oxides. The main goal was to develop methodologies that could assist in materials discovery or provide new insights into complex proton-conducting oxides. Through these methodologies, three new proton-conducting oxides, including both perovskite and non-perovskites, have been discovered. In terms of gaining insights, octahedral tilt/distortions and oxygen affinity are found to play a critical role in determining proton diffusivities and conductivities in doped barium zirconates. Replica exchange Monte Carlo approach has enabled to reveal realistic defect configurations, hydration behavior, and their temperature dependence in oxides. Our approach 'Materials discovery through interpretation', which integrates new insights or tendencies obtained from computations and experiments to sequential explorations of materials, has also identified perovskites that exhibit proton conductivity exceeding 0.01 S/cm and high chemical stability at 300
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