Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite

Abstract

This paper examines the cobalt-doped ceria/reduced graphene oxide (Co-CeO2/rGO) nanocomposite as a supercapacitor and modeling of its cyclic voltammetry behavior using Artificial Neural Network (ANN) and Random Forest Algorithm (RFA). Good agreement was found between experimental results and the predicted values generated by using ANN and RFA. Simulation results confirmed the accuracy of the models, compared to measurements from supercapacitor module power-cycling. A comparison of the best performance between ANN and RFA models shows that the ANN models performed better (value of coefficient of determination >0.95) than the RFA models for all datasets used in this study. The results of the ANN and RFA models could be useful in designing the unique nanocomposites for supercapacitors and other strategies related with energy and the environment.

Machine learning toward advanced energy storage devices and systems

“…The results show that ANN has the highest accuracy for predicting the capacitance, wheres GLR has the lowest accuracy. The performance of ANN and RF for estimating the current of Co-CeO 2 /rGO nanocomposite supercapacitors are compared in (Parwaiz et al, 2018). The training datasets are obtained from experiments.…”

“…Examples of the application of machine learning for capacitors and supercapacitors (A) The application of ML on the cyclic voltammetry behavior modeling of Co-CeO 2 /rGO nanocomposite supercapacitors. Reprinted with permission from(Parwaiz et al, 2018). Copyright 2018 American Chemical Society.…”

Physically soft magnetic films and devices: fabrication, properties, printability, and applications

“…These approaches will generate a huge set of data that can be subjected to machinelearning based approaches, eventually opening up avenues to investigate unexplored combinations of materials that lead to soft, magnetic inks and films with outstanding properties. Such machine learning based approaches for fabricating novel materials (especially nanocomposites with desired properties) have received a lot of attention recently; 163,[184][185][186][187][188][189][190] this will be one of the first approaches for fabricating novel soft, magnetic inks and films using machine learning. The third critical aspect that deserves significant attention is the flexibility in fabricating devices and components of various shapes using such soft magnetic inks and films.…”

Materials Informatics

“…They show that aCCHMs are suitable for understanding distortion-mode−property relationships whereas nCCHMs can be used to understand local mode−mode dependencies within a single composition NdNiO 3 and can guide experiments using nonisotropic perturbations. Khan and colleagues 5 described an application of Artificial Neural Network (ANN) and Random Forest (RF) to modeling of cyclic voltammetry behavior of Cobalt-doped Ceria/reduced graphene oxide (Co-CeO 2 /rGO) nanocomposite as supercapacitor. They showed that model predictions agreed with measurements from supercapacitor module power-cycling and suggested that their models can be used for rational design of novel nanocomposites for supercapacitors.…”