Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features.
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| Title: | Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features. |
|---|---|
| Authors: | Gong, Tianshun1 (AUTHOR), Yu, Weiyang2 (AUTHOR) yuweiyang@hpu.edu.cn, Wang, Xiangfu1 (AUTHOR) xfwang@njupt.edu.cn |
| Source: | Nanomaterials (2079-4991). Apr2026, Vol. 16 Issue 8, p478. 23p. |
| Subjects: | Machine learning, Electrode performance, Electric capacity, Supercapacitors, Global optimization, Shapley Additive Explanations, Electrodes |
| Abstract: | The development of high-performance composite supercapacitors remains challenging because the specific capacitance of composite electrodes is jointly governed by electronic percolation, ion accessibility, and interfacial contact, all of which are strongly affected by the balance among active materials, conductive agents, and binders. Traditional equivalent circuit modeling and empirical trial-and-error methods are often inadequate for describing these non-linear relationships and optimizing electrode design. To address this limitation, we establish a physics-guided and interpretable machine learning (ML) framework for predicting the specific capacitance of composite electrodes. Unlike traditional methods that rely on macroscopic mass fractions, our approach constructs a feature space comprising ten descriptors, including two newly introduced binder-related proxy descriptors—Binder-to-Conductive Ratio (BCR) and Specific Binder Loading (SBL)—to better represent the influence of binder content. By systematically evaluating 17 machine learning algorithms on a high-fidelity dataset, we identify the XGBoost model, optimized via Bayesian optimization, as the best predictor, achieving a coefficient of determination (R2) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials. [ABSTRACT FROM AUTHOR] |
| Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193463056 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gong%2C+Tianshun%22">Gong, Tianshun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Weiyang%22">Yu, Weiyang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yuweiyang@hpu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiangfu%22">Wang, Xiangfu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xfwang@njupt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nanomaterials+%282079-4991%29%22">Nanomaterials (2079-4991)</searchLink>. Apr2026, Vol. 16 Issue 8, p478. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Electrode+performance%22">Electrode performance</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+capacity%22">Electric capacity</searchLink><br /><searchLink fieldCode="DE" term="%22Supercapacitors%22">Supercapacitors</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Electrodes%22">Electrodes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The development of high-performance composite supercapacitors remains challenging because the specific capacitance of composite electrodes is jointly governed by electronic percolation, ion accessibility, and interfacial contact, all of which are strongly affected by the balance among active materials, conductive agents, and binders. Traditional equivalent circuit modeling and empirical trial-and-error methods are often inadequate for describing these non-linear relationships and optimizing electrode design. To address this limitation, we establish a physics-guided and interpretable machine learning (ML) framework for predicting the specific capacitance of composite electrodes. Unlike traditional methods that rely on macroscopic mass fractions, our approach constructs a feature space comprising ten descriptors, including two newly introduced binder-related proxy descriptors—Binder-to-Conductive Ratio (BCR) and Specific Binder Loading (SBL)—to better represent the influence of binder content. By systematically evaluating 17 machine learning algorithms on a high-fidelity dataset, we identify the XGBoost model, optimized via Bayesian optimization, as the best predictor, achieving a coefficient of determination (R2) of 0.981 and a low mean absolute percentage error (MAPE) of 14.49%. Importantly, interpretability analysis using Shapley Additive Explanations (SHAP) provides physically interpretable statistical insights, revealing that high BCR suppresses specific capacitance through an insulating barrier effect, whereas lattice distortion in the filler material promotes ion transport. This study offers a robust, data-driven framework for optimizing composite electrode performance, demonstrating the potential of interpretable ML models for the rational design of advanced energy-storage materials. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nanomaterials (2079-4991) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/nano16080478 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 478 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Electrode performance Type: general – SubjectFull: Electric capacity Type: general – SubjectFull: Supercapacitors Type: general – SubjectFull: Global optimization Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Electrodes Type: general Titles: – TitleFull: Prediction of Composite Supercapacitor Performance Through Combining Machine Learning with Novel Binder-Related Features. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gong, Tianshun – PersonEntity: Name: NameFull: Yu, Weiyang – PersonEntity: Name: NameFull: Wang, Xiangfu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20794991 Numbering: – Type: volume Value: 16 – Type: issue Value: 8 Titles: – TitleFull: Nanomaterials (2079-4991) Type: main |
| ResultId | 1 |
