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RESEARCH PAPER · Coordination Chemistry Reviews · 2024

Machine learning for gas–solid interaction materials and devices

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Machine learning for gas–solid interaction materials and devicesOriginalEnhancing machine learning in gas–solid interaction analysis: Addressing feature selection and dimensionality challenges3Reevaluating feature importance in gas–solid interaction predictions: A call for robust statistical methods3Mg-doped and Pt-modified NdFeO3 with machine learning for advanced CO2 detection1Soft materials nanoarchitectonics: liquid crystals, polymers, gels, biomaterials, and others7AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model6Machine learning-enabled sensor array using noble metal–functionalized WS 2 nanoflakes for drastic selective gas sensing43D truss absorbing metamaterial empowered by artificial intelligence: Reverse design and performance breakthrough0Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0A comprehensive analysis of occupational health and safety risks in civil aviation cargo: Insights from FF-DEMATEL0A data-driven multimodal deep learning framework for predicting gas-sensing performance of two-dimensional materials0

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  1. Enhancing machine learning in gas–solid interaction analysis: Addressing feature selection and dimensionality challenges

    Coordination Chemistry Reviews · 2025-03-03

    Yoshiyasu Takefuji

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  2. Reevaluating feature importance in gas–solid interaction predictions: A call for robust statistical methods

    Coordination Chemistry Reviews · 2025-03-05

    Yoshiyasu Takefuji

    Read original paper ↗ ·
  3. Mg-doped and Pt-modified NdFeO3 with machine learning for advanced CO2 detection

    Ceramics International · 2025-04-16

    Heng Zhang, Clyde Zhengdao Li, Feiyong Chen

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  4. Soft materials nanoarchitectonics: liquid crystals, polymers, gels, biomaterials, and others

    Beilstein Journal of Nanotechnology · 2025-07-04

    Katsuhiko Ariga

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  5. AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model

    Journal of environmental chemical engineering · 2025-11-01

    Tingfa Zhou, Peng Gui, Chao Hu, Weihua Xiao, Dong Liu, Zhiping Wang

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  6. Machine learning-enabled sensor array using noble metal–functionalized WS 2 nanoflakes for drastic selective gas sensing

    Chemical Engineering Journal · 2026-03-11

    Sukhwinder Singh, Jin-Young Kim, Eun Bi Kim, Eun Bi Kim, Eun Bi Kim, Nitish Kumar, Wansik Oum, Hyoun Woo Kim, Jong Heon Kim, Sang Sub Kim, Hyoun Woo Kim

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  7. 3D truss absorbing metamaterial empowered by artificial intelligence: Reverse design and performance breakthrough

    Sustainable materials and technologies · 2026-04-01

    Yan Hu, Senlin Tian, Yingjie Li

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  8. Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction

    Composites Science and Technology · 2026-05-07

    Yan Hu, Senlin Tian, Yingjie Li

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  9. A comprehensive analysis of occupational health and safety risks in civil aviation cargo: Insights from FF-DEMATEL

    Work · 2026-06-30

    Esmagül HAKKIOĞLU TÜYLÜOĞLU, Emine Can, Serap Tepe, Serkan Eti, Ahmet Çabuk

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  10. A data-driven multimodal deep learning framework for predicting gas-sensing performance of two-dimensional materials

    Microchemical Journal · 2026-09-26

    Shuai Liu, Yuhao Yan, Xiaonan Liang, Jiadu Wen, Chaochao Zhang, Guoxiang Chen

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