ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
0/0

Machine Learning Enabled Capacitance Prediction for Carbon-Based Supercapacitors

preprint
revised on 04.05.2018 and posted on 07.05.2018 by Shan Zhu, Jiajun Li, Liying Ma, Chunnian He, Enzuo Liu, Fang He, Chunsheng Shi, Naiqin Zhao
Carbon is the most widely used electrode for the supercapacitors. To predict the capacitance of carbon-based supercapacitors, this work applies three machine learning (ML) methods, including linear regression, Lasso and artificial neural network. For training the ML process, we extracted data from hundreds of published papers. Moreover, five variables were selected to figure out their impact on capacitance, including specific surface area, calculated pore size, ID/IG ratio, N-doping level and voltage window. By evaluated with the real data, all of three methods achieve acceptable prediction results, and ANN exhibits the best performance. More importantly, this work shows the potential of ML in material science and advanced applications.

History

Email Address of Submitting Author

shanzhu@tju.edu.cn

Institution

Tianjin University

Country

China

ORCID For Submitting Author

0000-0001-6803-1338

Declaration of Conflict of Interest

There is no conflict of interest.

Exports