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RESEARCH PAPER · Crystals · 2022

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

Hartmut Schlenz, Stefan Baumann, Wilhelm Albert Meulenberg, Olivier Guillon

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10 direct citing papers in this saved network. Explore the next layer with second-order citations.

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The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine LearningOriginalApplications of Machine Learning to the Study of Crystalline Materials0Structural, morphological, optical and dielectric properties of sodium bismuth titanate ceramics36Impact of oxygen vacancies on thermal and electronic transport of donor-doped CaMnO3-6Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO3-δ Perovskites with High Oxide Ionic Conductivity11Machine Learning Design of Perovskite Catalytic Properties83Machine Learning-Based Prediction of Stability in High-Entropy Nitride Ceramics10Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification8Comprehensive first-principle investigation of sodium protactinium oxide (NaPaO3): Unraveling structural, electrical, mechanical, and thermodynamic properties under hydrostatic pressure8Research Progress of Computational Fluid Dynamics in Mixed Ionic–Electronic Conducting Oxygen-Permeable Membranes7Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells1

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  1. Applications of Machine Learning to the Study of Crystalline Materials

    Crystals · 2022-07-30

    Hartmut Schlenz, Stefan Sandfeld

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  2. Structural, morphological, optical and dielectric properties of sodium bismuth titanate ceramics

    Inorganic Chemistry Communications · 2022-11-04

    Najah Rhimi, N. Dhahri, Mabrouk Khelifi, E.K. Hlil, Jemai Dhahri

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  3. Impact of oxygen vacancies on thermal and electronic transport of donor-doped CaMnO3-

    Journal of Solid State Chemistry · 2023-07-20

    Oleg V. Merkulov, I.V. Shamsutov, М. А. Рыжков, B.V. Politov, Inna Victorovna Baklanova, Evgueni V. Chulkov, Vladlen P. Zhukov

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  4. Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO3-δ Perovskites with High Oxide Ionic Conductivity

    The Journal of Physical Chemistry C · 2023-08-18

    Pengcheng Xu, Tian Lu, Xiaobo Ji, Minjie Li, Wencong Lu

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  5. Machine Learning Design of Perovskite Catalytic Properties

    Advanced Energy Materials · 2024-02-06

    Ryan M. Jacobs, Jian Liu, Harry Abernathy, Dane D. Morgan

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  6. Machine Learning-Based Prediction of Stability in High-Entropy Nitride Ceramics

    Crystals · 2024-04-30

    Tianyu Lin, Ruolan Wang, Dazhi Liu

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  7. Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification

    Royal Society Open Science · 2024-05-01

    Quan Luo, Hua Hao, Hanxing Liu

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  8. Comprehensive first-principle investigation of sodium protactinium oxide (NaPaO3): Unraveling structural, electrical, mechanical, and thermodynamic properties under hydrostatic pressure

    Physics Open · 2025-01-27

    Md Hossen, Istiak Ahmed Ovi, Md Anas Bin Hossen, Md Anas Bin Hossen, Sharif Hossain

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  9. Research Progress of Computational Fluid Dynamics in Mixed Ionic–Electronic Conducting Oxygen-Permeable Membranes

    Membranes · 2025-06-27

    Jun Liu, Jing Zhao, Yulu Liu, Yongfan Zhu, Wanglin Zhou, Zhenbin Gu, Guangru Zhang, Zhengkun Liu

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  10. Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells

    Energy and AI · 2026-01-29

    Tan Le Dinh, Hartmut Schlenz, Norbert Heribert Menzler, Alejandro A. Franco, Olivier Guillon

    Read original paper ↗ ·
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