了解使用人工智能的基于CNT的超级电容电极的特定电容.
Wael Z Tawfik1, Mohamed Shaban2, Athira Raveendran3
1Physics Department, Faculty of Science, Beni-Suef University Beni-Suef 62511 Egypt wael.farag@science.bsu.edu.eg.
RSC advances
|January 31, 2025
概括
人工神经网络 (ANN) 准确预测了碳纳米管 (CNT) 超级电容器电容,超过了其他机器学习模型. 灵敏度分析揭示了影响超级电容性能的关键因素.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算科学 计算科学
背景情况:
- 超级电容器是重要的储能设备.
- 碳纳米管 (CNTs) 为超级电容器应用提供了独特的特性.
- 预测建模可以优化超级电容器的设计和性能.
研究的目的:
- 用各种机器学习算法预测CNT超级电容器的特定电容.
- 为了比较人工神经网络 (ANN) 的准确性和可靠性,随机森林回归 (RFR),k-最近邻居回归 (KNN) 和决策树回归 (DTR).
- 通过灵敏度分析识别影响超级电容器特定电容性的关键输入参数.
主要方法:
- 对CNT超级电容器的实验数据被用于培训和验证.
- 机器学习模型包括ANN,RFR,KNN和DTR用于预测.
- 对于敏感性分析,使用了夏普利添加式解释 (SHAP) 框架.
主要成果:
- 该ANN算法在预测特定电容方面表现出卓越的准确性,达到R平方值约为0.91和根平均平方误差 (RMSE) 为26.24.
- 决策树回归模型 (DTR) 的可靠性最低,RMSE为53.46和R平方为0.63.
- 灵敏度分析表明了诸如孔隙结构,特定表面积和特定电容上的ID/IG比率等参数的相对重要性.
结论:
- 对于准确预测基于CNT的超级电容器的特定电容,ANN模型非常有效.
- 机器学习,特别是ANN,显示出优化先进能源存储设备设计的巨大潜力.
- 通过灵敏度分析了解材料性能的影响对于提高超级电容器性能至关重要.
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