通过堆叠模型和SHAP导向优化,释放异原子合石墨烯基超级电容器的全部潜力
Krittapong Deshsorn1, Krittamate Payakkachon1, Tanapat Chaisrithong1
1School of Bio-Chemical Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.
机器学习优化了石墨烯超级电容器的能量存储. 高容量需要大面积,特定的异质原子兴奋剂 (,氧,硫),受控的缺陷,酸电解质和低电流密度以提高性能.
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:
- 石墨烯超级电容器提供卓越的能量存储.
- 异原子兴奋剂增强了石墨烯电极的性能.
- 最佳的兴奋剂条件和协同效应仍然不清楚.
研究的目的:
- 使用机器学习来预测石墨烯超级电容器电容.
- 为了确定高容量石墨烯电极的最佳条件.
- 为电化学研究提供计算工具.
主要方法:
- 应用了各种机器学习模型 (轻梯度提升机,极端梯度提升,随机森林等). 用于电容预测.
- 开发了一种堆叠组合模型,以提高预测准确度.
- 使用SHAP值来确定关键属性对电容的影响.
主要成果:
- 确定了高容量的最佳参数:大特定表面积 (SA),4 - 5%的,10 - 15%的氧,高硫含量,缺陷比接近1,酸电解质和低电流密度.
- 开发并验证了一个预测性机器学习模型.
- 量化了各种参数对电容的影响.
结论:
- 机器学习有效地预测和优化石墨烯超级电容器的性能.
- 制定了设计高容量石墨烯电极的准则.
- 开发的模型和代码为未来的研究和应用提供了宝贵的资源.
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