Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors
Understanding the relationships between design factors is crucial for the development of hydrogel supercapacitors, yet the relative importance and interdependencies of material properties and operating conditions remain unclear. This study employs interpretable machine learning to analyze the design...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Gels |
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| Online Access: | https://www.mdpi.com/2310-2861/11/6/464 |
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| author | Liying Xu Siqi Liu Dandan Hu Junhao Liu Yuze Zhang Ziqiang Li Zichuan Su Daxin Liang |
| author_facet | Liying Xu Siqi Liu Dandan Hu Junhao Liu Yuze Zhang Ziqiang Li Zichuan Su Daxin Liang |
| author_sort | Liying Xu |
| collection | DOAJ |
| description | Understanding the relationships between design factors is crucial for the development of hydrogel supercapacitors, yet the relative importance and interdependencies of material properties and operating conditions remain unclear. This study employs interpretable machine learning to analyze the design factors that affect hydrogel supercapacitor performance, using 232 experimental samples from 41 recent studies. SHAP analysis was implemented to quantify parameter importance and reveal feature interactions among 16 key design parameters, including polymer types, electrolyte formulations, and operating conditions. Results show that synthetic vinyl polymers most strongly influence specific capacitance, while conductive polymers predominantly affect cycle stability. Ionic conductivity emerged as the most impactful parameter despite moderate feature importance, indicating complex nonlinear relationships. Critical interdependencies between polymer concentration and electrolyte formulation suggest that optimal design requires coordinated parameter selection rather than independent optimization. This interpretable framework provides quantitative insights into design factor hierarchies and parameter interdependencies, offering evidence-based guidelines for rational material selection in hydrogel supercapacitor development. |
| format | Article |
| id | doaj-art-458e6c7fe4e34c19960f282fdb76394c |
| institution | Kabale University |
| issn | 2310-2861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Gels |
| spelling | doaj-art-458e6c7fe4e34c19960f282fdb76394c2025-08-20T03:27:02ZengMDPI AGGels2310-28612025-06-0111646410.3390/gels11060464Interpretable Machine Learning Analysis of Design Factors in Hydrogel SupercapacitorsLiying Xu0Siqi Liu1Dandan Hu2Junhao Liu3Yuze Zhang4Ziqiang Li5Zichuan Su6Daxin Liang7School of Food Engineering, Harbin University, Harbin 150086, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaCollege of Chemistry and Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, ChinaKey Laboratory of Bio-Based Material Science and Technology (Ministry of Education), Northeast Forestry University, Harbin 150040, ChinaUnderstanding the relationships between design factors is crucial for the development of hydrogel supercapacitors, yet the relative importance and interdependencies of material properties and operating conditions remain unclear. This study employs interpretable machine learning to analyze the design factors that affect hydrogel supercapacitor performance, using 232 experimental samples from 41 recent studies. SHAP analysis was implemented to quantify parameter importance and reveal feature interactions among 16 key design parameters, including polymer types, electrolyte formulations, and operating conditions. Results show that synthetic vinyl polymers most strongly influence specific capacitance, while conductive polymers predominantly affect cycle stability. Ionic conductivity emerged as the most impactful parameter despite moderate feature importance, indicating complex nonlinear relationships. Critical interdependencies between polymer concentration and electrolyte formulation suggest that optimal design requires coordinated parameter selection rather than independent optimization. This interpretable framework provides quantitative insights into design factor hierarchies and parameter interdependencies, offering evidence-based guidelines for rational material selection in hydrogel supercapacitor development.https://www.mdpi.com/2310-2861/11/6/464hydrogel supercapacitormachine learninginterpretable prediction |
| spellingShingle | Liying Xu Siqi Liu Dandan Hu Junhao Liu Yuze Zhang Ziqiang Li Zichuan Su Daxin Liang Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors Gels hydrogel supercapacitor machine learning interpretable prediction |
| title | Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors |
| title_full | Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors |
| title_fullStr | Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors |
| title_full_unstemmed | Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors |
| title_short | Interpretable Machine Learning Analysis of Design Factors in Hydrogel Supercapacitors |
| title_sort | interpretable machine learning analysis of design factors in hydrogel supercapacitors |
| topic | hydrogel supercapacitor machine learning interpretable prediction |
| url | https://www.mdpi.com/2310-2861/11/6/464 |
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