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Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models.

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第一作者： Yin Kan,Phua

第一单位： Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.

作者： Yin Kan,Phua ^[1] ; Tsuyohiko,Fujigaya ^[2] ; Koichiro,Kato ^[3]

作者单位： Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, Japan. ^[1] Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.;International Institute for Carbon Neutral Energy Research, Kyushu University, Fukuoka, Japan.;Center for Molecular Systems, Kyushu University, Fukuoka, Japan. ^[2] Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.;Center for Molecular Systems, Kyushu University, Fukuoka, Japan.;Research Institute for Information Technology, Kyushu University, Fukuoka, Japan. ^[3]

关键词 Machine learning models anion exchange membrane data-driven explainable AI fuel cell functional polymers

DOI 10.1080/14686996.2023.2261833

PMID 37854121

发布时间 2023-10-20

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Science and technology of advanced materials

2023年24卷1期

2261833页

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Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models.

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Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models.

Science and technology of advanced materials

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Science and technology of advanced materials

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