Predicting the capacitance of carbon-based electric double layer capacitors by machine learning

Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and sc...

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Detalles Bibliográficos
Autores principales: Su, Haiping, Lin, Sen, Deng, Shengwei, Lian, Cheng, Shang, Yazhuo, Liu, Honglai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2019
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419274/
https://www.ncbi.nlm.nih.gov/pubmed/36131961
http://dx.doi.org/10.1039/c9na00105k
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author Su, Haiping
Lin, Sen
Deng, Shengwei
Lian, Cheng
Shang, Yazhuo
Liu, Honglai
author_facet Su, Haiping
Lin, Sen
Deng, Shengwei
Lian, Cheng
Shang, Yazhuo
Liu, Honglai
author_sort Su, Haiping
collection PubMed
description Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials.
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spelling pubmed-94192742022-09-20 Predicting the capacitance of carbon-based electric double layer capacitors by machine learning Su, Haiping Lin, Sen Deng, Shengwei Lian, Cheng Shang, Yazhuo Liu, Honglai Nanoscale Adv Chemistry Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials. RSC 2019-04-25 /pmc/articles/PMC9419274/ /pubmed/36131961 http://dx.doi.org/10.1039/c9na00105k Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/
spellingShingle Chemistry
Su, Haiping
Lin, Sen
Deng, Shengwei
Lian, Cheng
Shang, Yazhuo
Liu, Honglai
Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title_full Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title_fullStr Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title_full_unstemmed Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title_short Predicting the capacitance of carbon-based electric double layer capacitors by machine learning
title_sort predicting the capacitance of carbon-based electric double layer capacitors by machine learning
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419274/
https://www.ncbi.nlm.nih.gov/pubmed/36131961
http://dx.doi.org/10.1039/c9na00105k
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AT dengshengwei predictingthecapacitanceofcarbonbasedelectricdoublelayercapacitorsbymachinelearning
AT liancheng predictingthecapacitanceofcarbonbasedelectricdoublelayercapacitorsbymachinelearning
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