Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes

Aug 2, 2026

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Graphene oxide (GO) intercalated with transition metal oxides has been studied to achieve improved supercapacitance, but identifying an optimal composition requires many experiments. This work integrates a machine learning random forest model with experimentally observed X-ray photoelectron spectroscopy data to build a chemical analysis dataset for Co(III)/Co(II) ratios in thermally synthesized Co-rGO supercapacitor electrodes. The ML-predicted dataset supports capacitance modeling and yields R2=0.9655 and MSE=6.77, while prediction error stays under 8% versus experimental validation. The approach reduces resource consumption and can guide further experimental and computational analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

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Introduction

Motivation and challenges in characterizing TMOs-GO supercapacitors

ML-assisted material characterization and data-driven prediction approaches

Carbon 214 (2023) 118342 Contents lists available at ScienceDirect Carbon journal [homepage: www.elsevier.com/locate/carbon](homepage: www.elsevier.com/locate/carbon) | Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes | | | | | --- | --- | --- | --- | | Xiaoyu Liu a, 1, Dali Jia, **, 1, Xiaoheng Jin a, Vanesa Quintanoa, b, Rakesh Joshia, *
a School of Materials Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
b Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193, Barcelona, Spain | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords: Graphene oxide Supercapacitance Random forest Machine learning | | Graphene oxide (GO) intercalated with transition metal oxides (TMOs) has been investigated for optimal supercapacitance performance. However, attaining the best performance requires conducting numerous experiments to find an optimal material composition. This raises an important question; can resource consumption associated with extensive experiments be minimized? Here, we combine the machine learning (ML)-based random forest (RF) model with experimentally observed X-ray photoelectron spectroscopy (XPS) data to construct the complete chemical analysis dataset of Co(III)/Co(II) ratio for thermally synthesized Co-rGO supercapacitor electrodes. The ML predicted dataset could be further coupled with other experiment results, such as cyclic voltammetry (CV), to establish a precise model for predicting capacitance, with ML coefficient of determination (R2) value of 0.9655 and mean square error value of 6.77. Furthermore, the error between predicted capacitance and experimental validation is found to be less than 8%. Our work indicates that RF can be used to predict XPS data for the TMO-GO system, thereby reducing experimental resource consumption for materials analysis. Moreover, the RF-predicted result can be further utilized in experimental and computational analysis. | | 1. Introduction The development of sustainable and renewable energy storage systems has gained significant momentum in recent years, leading to the emergence of non-conventional energy devices such as supercapacitors. Graphene-based supercapacitors have emerged as promising candidates for next-generation energy storage technology because of their high specific area, fast-charging capability, long-life cycle, and low maintenance cost [1–5]. Among these, the combination of graphene oxide (GO) with transition metal oxides (TMOs-GO) has garnered significant attention in recent years [6–9]. Notably, the integration of cobalt oxide (Co oxide) and reduced graphene oxide (rGO) electrodes have demonstrated remarkable potential [10]. The exceptional properties of rGO, including high surface area [11] and facile functionalization [12], coupled with the benefits of Co oxide, such as the introduction of active sites [13,14], improved conductivity [15], and widened voltage window, make this hybrid electrode a highly desirable choice. Nevertheless, the investigation of these TMOs-GO supercapacitor materials presents significant challenges. Due to various synthesis conditions, materials containing different structures and compositions exceed the scope of exhaustive methods [16,17]. The synergistic interactions among components contribute to the complexity of the system, limiting the ability to predict results intuitively [18,19]. It is technically infeasible to rely solely on experiments to characterize the material and predict the capacitance under all experimental conditions, considering prediction accuracy, time efficiency, and cost-effectiveness [16,17]. Hence, utilizing data-driven methods like machine learning (ML) to accelerate the prediction process is necessary [20]. Multiple ML studies have exhibited that it can predict capacitance, which still relies heavily

FAQs

Why are many experiments required to optimize TMOs-GO supercapacitance performance?

Because different synthesis conditions produce diverse structures and compositions, extensive experimental work is needed to search for the optimal material configuration and composition.

How does the study use machine learning in chemical characterization?

It combines a random forest (RF) model with XPS measurements to construct a chemical analysis dataset, specifically targeting the Co(III)/Co(II) ratio for Co-rGO electrodes.

What accuracy does the ML-based capacitance prediction achieve?

The model reports R2=0.9655 and MSE=6.77, and the discrepancy between predicted capacitance and experimental validation is less than 8%.

Aug 2, 2026

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