Ab-initio calculation driven machine learning based prediction of quantum capacitance of titanium-doped graphene for efficient supercapacitor electrode design DOI
N.C. Mishra, Naresh Bahadursha,

Abbidi Shivani Reddy

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 107, P. 115038 - 115038

Published: Dec. 27, 2024

Language: Английский

Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions DOI Creative Commons

Guangheng Qi,

Ning Ma,

Kai Wang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(11), P. 2585 - 2585

Published: May 27, 2024

With the rapid development of new energy industry, supercapacitors have become key devices in field storage. To forecast remaining useful life (RUL) supercapacitors, we introduce a technology that integrates variational mode decomposition (VMD) with bidirectional long short-term memory (BiLSTM) neural network. Firstly, aging experiments under various temperatures and voltages were carried out to obtain data. Then, VMD was implemented decompose data, which helped eliminate disturbances, including capacity recovery test errors. hyperparameters BiLSTM adjusted, employing sparrow search algorithm (SSA) improve consistency between input data network structure. After obtaining optimal BiLSTM, decomposed into for prediction. The experimental results showed VMD-SSA-BiLSTM model proposed this paper has high prediction accuracy robustness different voltages, an average RMSE 0.112519, decrease 44.3% compared minimum 0.031426.

Language: Английский

Citations

38
Data-based modeling for prediction of supercapacitor capacity: Integrated machine learning and metaheuristic algorithms DOI
Hamed Azimi, Ebrahim Ghorbani‐Kalhor, Seyed Reza Nabavi

et al.

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2025, Volume and Issue: 170, P. 105996 - 105996

Published: Jan. 31, 2025

Language: Английский

Citations

2
Temperature-dependent performance prediction for cerium oxynitride solid-state symmetric supercapacitor using machine learning DOI
Sourav Ghosh,

Ashwath Sibi,

G. Sudha Priyanga

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115562 - 115562

Published: Jan. 29, 2025

Language: Английский

Citations

1
Insights into the specific capacitance of CNT-based supercapacitor electrodes using artificial intelligence DOI Creative Commons
Wael Z. Tawfik, Mohamed Shaban, Athira Raveendran

et al.

RSC Advances, Journal Year: 2025, Volume and Issue: 15(5), P. 3155 - 3167

Published: Jan. 1, 2025

This study uses various ML algorithms, including artificial neural networks, random forest, k -nearest neighbors, and decision tree, based on experimental studies to predict the specific capacitance characteristics of CNT-based SC electrodes.

Language: Английский

Citations

1
A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials DOI
Chuang Wang, Xingxing Cheng, Kai Luo

et al.

Materials Science and Engineering R Reports, Journal Year: 2025, Volume and Issue: 165, P. 101010 - 101010

Published: May 3, 2025

Language: Английский

Citations

1
Deep characterization of the electrical features of Ag/P3HT/SiNWs Schottky diodes by machine learning models based on experimental study DOI

Radhouane Laajimi,

K. Laajimi, Mehdi Rahmani

et al.

Surfaces and Interfaces, Journal Year: 2024, Volume and Issue: unknown, P. 105175 - 105175

Published: Sept. 1, 2024

Language: Английский

Citations

4
Machine learning-assisted prediction, screen, and interpretation of porous carbon materials for high-performance supercapacitors DOI Open Access
Hongwei Liu, Zhenming Cui,

Zhennan Qiao

et al.

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: 4(4)

Published: Oct. 24, 2024

Porous carbon materials (PCMs) are preferred as electrode for supercapacitor energy storage applications due to their superior characteristics. However, the optimal performance of these electrodes requires trial and error experimental exploration complexity influencing factors. To address this limitation, we develop a machine learning (ML) combined validation approach predict, screen interpret ideal PCMs supercapacitors. Four ML models used predicting specific capacitance (SC) properties light gradient boosting (LGBM) model exhibits best prediction with an R2 value 0.92. Through comprehensive interpretability analysis ML, important variables SC identified impact range is determined. By analyzing deviation key values during verification, accurate predictions made, facilitating precise material screening. Additionally, accuracy applicability evaluated. This research pioneered eigenvalue fall-point screening based on combination experiments accurately materials, providing compelling strategy advancing technology.

Language: Английский

Citations

4
Energy storage in supercapacitor researches: Interdisciplinary applications from molecular simulations to machine learning DOI

Yawen Dong,

Yutong Liu, Fei‐Fei Mao

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 393, P. 126074 - 126074

Published: May 10, 2025

Language: Английский

Citations

0
Advancements on the synthesis and modification of metal-organic framework derivatives for supercapacitors DOI
Geping He, X. R. Li, Wenbo Zhou

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 127, P. 117090 - 117090

Published: May 23, 2025

Language: Английский

Citations

0
Ab-initio calculation driven machine learning based prediction of quantum capacitance of titanium-doped graphene for efficient supercapacitor electrode design DOI
N.C. Mishra, Naresh Bahadursha,

Abbidi Shivani Reddy

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 107, P. 115038 - 115038

Published: Dec. 27, 2024

Language: Английский

Citations

1