通过机器学习加速探索高性能矿太阳能电池的顶部接口化器
Zhikang Zhu1, Zhixu Zhou1, Yue Zang1
1Institute of Carbon Neutrality and New Energy, School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, P. R. China.
Small (Weinheim an der Bergstrasse, Germany)
|January 31, 2026
概括
机器学习加速了对矿太阳能电池 (PSC) 的有效被动器的发现. 该框架确定了最佳的盐,并提供了设计规则,提高了设备的性能和效率.
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.
- 计算化学的计算化学
背景情况:
- 接口被动化对于减少矿太阳能电池 (PSC) 缺陷至关重要.
- 潜在的被动剂的巨大化学空间使得高效的发现变得复杂.
- 开发有效的被动器需要理解结构-属性关系.
研究的目的:
- 开发一个以机器学习 (ML) 为指导的工作流程,以加快有效的盐受体器的识别.
- 创建一个适用于n-i-p和p-i-n矿太阳能电池配置的建筑意识框架.
- 为选择被动器提供具有物理意义的设计规则.
主要方法:
- 策划了296个关于被动器 (2017-2024) 的文献记录的数据集.
- 用分子指纹和描述器集进行ML模型训练的被动器.
- 训练并评估了72个回归模型,包括XGBoost和支持向量回归 (SVR).
- 利用夏普利添加式解释 (SHAP) 进行特征重要性分析.
主要成果:
- 确定了表现最好的ML模型:256&XGBoost用于n-i-p和RDKit&SVR用于p-i-n架构.
- SHAP分析揭示了关键驱动因素:被动剂度,分子量 (120-280 g/mol),极性和化物组成 (I/Br比).
- 实验验证显示,对选定的被动器的改善比为1.152,与预测的1.134.4非常接近.
结论:
- 建立了一个可转移的,以SHAP为指导的ML框架,用于高效地选接口被动器.
- 证明了框架能够为矿太阳能电池提供物理上有意义的设计规则的能力.
- 成功加速了高性能被动器的发现,提高了设备的效率.
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