Graphene as an Active Material for Supercapacitors: A Machine Learning Approach

Anif Jamaluddin, Annisa Dwi Nursanti, Anafi Nur'aini, Rekyan Regasari M Putri, Muhammad Usama Arshad


Graphene is a promising material for supercapacitors due to its unique properties, which influence the device's supercapacitor. This study aims to investigate the key factor of graphene properties in supercapacitors (, with the goal of improving their performance. Also, we observe the machine learning models for predicting capacitance of supercapacitor including four algorithms of machine learning: Linear Regression (LR), lazy IBK, Decision Table (DT), and Random Forest (RF). Machine learning model showed that the RF model demonstrated the highest correlation value of 0.745, surpassing other models. Also, the study revealed that graphene has a high specific surface area and highly porous structure, which enhanced the high capacitance values. Finally, these machine learning models are suitable to apply in materials sciences field for understanding the materials properties in supercapacitor.


Graphene; Machine Learning; Surface area; Supercapacitors

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