Hyperparameter tuned machine learning predictions of specific capacitance of conducting polymers and their composites for high performance advanced supercapacitors

IF 2.5 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Applied Physics A Pub Date : 2024-12-29 DOI:10.1007/s00339-024-08137-8
Mashqoor Alam, Samina Husain
{"title":"Hyperparameter tuned machine learning predictions of specific capacitance of conducting polymers and their composites for high performance advanced supercapacitors","authors":"Mashqoor Alam,&nbsp;Samina Husain","doi":"10.1007/s00339-024-08137-8","DOIUrl":null,"url":null,"abstract":"<div><p>This research investigates the use of machine learning (ML) to improve the performance of conducting polymer-based electrodes in supercapacitors, which leverage both electric double-layer capacitance (EDLC) and pseudocapacitive characteristics. Six ML models—Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—are evaluated for their ability to predict the specific capacitance of electrodes using an experimental dataset comprising Polyaniline (PANI), Polypyrrole (Ppy), and Polythiophene (PTh). Performance metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R<sup>2</sup>). Among the models, MLP demonstrates superior predictive accuracy, achieving the lowest MAE of 0.1452 and MSE of 0.0373, along with the highest R<sup>2</sup> of 0.9622. In contrast, Decision Tree and SVM exhibited higher error values, with MAEs of 0.2107 and 0.2267 and R<sup>2</sup> values around 0.885. Although Random Forest and XGBoost achieved competitive R<sup>2</sup> values of 0.9399 and 0.9354, their errors are comparatively higher than MLP. These results highlight the effectiveness of advanced ML techniques in enhancing supercapacitor technology and indicate the potential of these models to predict and optimize conducting polymer-based electrode materials for improved performance.</p></div>","PeriodicalId":473,"journal":{"name":"Applied Physics A","volume":"131 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Physics A","FirstCategoryId":"4","ListUrlMain":"https://link.springer.com/article/10.1007/s00339-024-08137-8","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

This research investigates the use of machine learning (ML) to improve the performance of conducting polymer-based electrodes in supercapacitors, which leverage both electric double-layer capacitance (EDLC) and pseudocapacitive characteristics. Six ML models—Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—are evaluated for their ability to predict the specific capacitance of electrodes using an experimental dataset comprising Polyaniline (PANI), Polypyrrole (Ppy), and Polythiophene (PTh). Performance metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2). Among the models, MLP demonstrates superior predictive accuracy, achieving the lowest MAE of 0.1452 and MSE of 0.0373, along with the highest R2 of 0.9622. In contrast, Decision Tree and SVM exhibited higher error values, with MAEs of 0.2107 and 0.2267 and R2 values around 0.885. Although Random Forest and XGBoost achieved competitive R2 values of 0.9399 and 0.9354, their errors are comparatively higher than MLP. These results highlight the effectiveness of advanced ML techniques in enhancing supercapacitor technology and indicate the potential of these models to predict and optimize conducting polymer-based electrode materials for improved performance.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
高性能先进超级电容器中导电聚合物及其复合材料比电容的超参数调谐机器学习预测
本研究探讨了利用机器学习(ML)来提高超级电容器中导电聚合物电极的性能,该超级电容器利用双电层电容(EDLC)和伪电容特性。六个ML模型-支持向量机(SVM),随机森林(RF),多层感知器(MLP),决策树(DT),极端梯度增强(XGBoost)和人工神经网络(ANN) -使用包含聚苯胺(PANI),聚吡啶(Ppy)和聚噻吩(PTh)的实验数据集评估其预测电极特定电容的能力。性能指标包括平均绝对误差(MAE)、均方误差(MSE)和r平方(R2)。其中,MLP模型的预测准确率较高,MAE最低为0.1452,MSE为0.0373,R2最高为0.9622。相比之下,决策树和支持向量机的误差值更高,MAEs分别为0.2107和0.2267,R2在0.885左右。虽然Random Forest和XGBoost的竞争R2值分别为0.9399和0.9354,但它们的误差相对高于MLP。这些结果突出了先进ML技术在增强超级电容器技术方面的有效性,并表明这些模型在预测和优化导电聚合物基电极材料以提高性能方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Applied Physics A
Applied Physics A 工程技术-材料科学:综合
CiteScore
4.80
自引率
7.40%
发文量
964
审稿时长
38 days
期刊介绍: Applied Physics A publishes experimental and theoretical investigations in applied physics as regular articles, rapid communications, and invited papers. The distinguished 30-member Board of Editors reflects the interdisciplinary approach of the journal and ensures the highest quality of peer review.
期刊最新文献
Microwave-assisted rapid synthesis of yttrium iron garnet nano powders: formation mechanism and magnetic properties Tensile mechanical behavior of tungsten fiber network reinforced tungsten-copper composites: a numerical simulation study DFT and experimental investigations on structural, electronic, thermoelectric, and optical properties of Zn doped PbS NiO nanosheet-assembled chemiresistive sensor for NO2 detection Green synthesis of Cr3+ doped nickel ferrite nanoparticles and their photocatalytic applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1