Ensemble Approach for Capacitance Prediction of Heteroatom Doped Carbon Based Electrode Materials

Authors Richa Dubey , Velmathi Guruviah, Ravi Prakash Dwivedi
Affiliations

Vellore Institute of Technology VIT Chennai, Kelambakkam – Vandalur Rd, Rajan Nagar, Chennai, Tamil Nadu, India
Е-mail richa.dubey@vit.ac.in
Issue Volume 15, Year 2023, Number 3
Dates Received May 2023; revised manuscript received 12 June 2023; published online 30 June 2023
Citation Richa Dubey, Velmathi Guruviah, Ravi Prakash Dwivedi, J. Nano- Electron. Phys. 15 No 3, 03011 (2023)
DOI https://doi.org/10.21272/jnep.15(3).03011
PACS Number(s) 42.90. + p, 45.10.Hj, 66.30. – h
Keywords Carbon based electrode, Energy storage, Heteroatom-doped, Machine learning, Nitrogen doped, Supercapacitor (3) .
Annotation

An ensemble approach-based machine learning modeling is used in the current study for unveiling the effect of various electrode parameters on the electrochemical performance of hetero-atom doped nanocarbons. This is achieved using three meta-classifiers in combination with traditional Multi-Layer Perceptron and Random Forest models. The three meta-classifiers used are namely (i) bagging, (ii) classification via regression (CVR) and (iii) multi class classifier (MCC). Amongst these three models, bagging and classification via regression provided greater accuracy in terms of correctly classified instances (%) and area under region of convergence values. The designed models are used to predict class of specific capacitance values. 94.5 % of the considered dataset is classified correctly proving a better accuracy of the designed models. Lowest root mean square value of 0.1787 was obtained for RF model. Compared to the models defined in the literature, the suggested models in this work provide best fit of the experiment and predicted values with highest accuracy and lowest error performance values. The lowest error value for RF and MLP models are 0.18 and 0.19 respectively.

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