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数据驱动的优化和机械评估的矿太阳能电池通过堆叠组合和SHAP可解释性

Ruichen Tian^1,2, Aldrin D Calderon^1,2, Quanrong Fang³

¹School of Mechanical, Manufacturing, and Energy Engineering, Mapua University, Muralla Street, Intramuros, Manila 1004, Philippines.

Materials (Basel, Switzerland)

|September 27, 2025

概括

一个基于数据的模型加速了矿太阳能电池 (PSC) 设计,预测了性能和机械强度. 这种方法减少了实验力度,使得可持续光伏技术的高效开发成为可能.

科学领域:

材料科学材料科学材料科学
可再生能源可再生能源是可再生能源.
计算建模计算建模

背景情况:

矿太阳能电池 (PSC) 提供高功率转换效率 (PCE) 和材料多功能性.
传统的PSC优化依赖于缓慢,代的实验方法.
加快设计高效和强大的PSC对于推进光伏技术至关重要.

研究的目的:

引入数据驱动的建模策略,以加速设计高效且机械稳固的PSC.
评估监督回归模型来预测光伏关键参数.
通过实验制造和机械评估来验证预测模型.

主要方法:

评估了七种监督回归模型,用于预测PCE,Jsc,Voc和FF.
采用堆叠组合框架,以获得更高的预测准确度 (PCE的R2=0.8577).
利用SHAP分析来分析模型的可解释性,确定关键的有影响力的参数.
进行了多层次的机械评估 (曲,压缩,冲击,粘附,纳米痕).

主要成果:

堆叠组合模型在预测光伏参数方面取得了很高的准确性.
SHAP分析确定了前体溶剂成分,A位点阴离子比率和HTL添加剂作为关键因素.
基于模型洞察力的制造设备实现了最大PCE的24.9%.
优化的PSC显示了增强的界面稳定性和抗破裂性.

结论:

数据驱动的预测实验框架有效地加速了PSC设计的性能和可靠性.
这种方法为开发可扩展和持久的矿太阳能电池技术提供了基础.
计算建模与实验验证相结合,是下一代光伏的关键.

关键词:

SHAP的解释性可以解释.通过机器学习引导的设计.机械可靠性机械可靠性矿太阳能电池是如何使用的光伏的性能优化,光伏的性能优化.堆叠组合学习学习堆叠组合学习

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