大型语言模型辅助的添加物选择,用于有效的倒置矿太阳能电池中的协同缺陷和结晶控制.
Zhirui Chen1, Xinde Wang2, Jiaqi Wang1
1Key Laboratory of Advanced Light Conversion Materials and Biophotonics, Department of Chemistry, School of Chemistry and Life Resources, Renmin University of China, Beijing, P. R. China.
ChemSusChem
|February 8, 2026
概括
研究人员使用矿-R1大型语言模型发现乙基2-氨基酸化物 (EAH) 作为矿太阳能电池的高效添加剂,提高效率和稳定性.
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.
- 人工智能的人工智能
背景情况:
- 矿太阳能电池需要前体添加剂来提高效率和稳定性.
- 目前的添加剂选择依赖于低效的试错方法.
研究的目的:
- 利用一个大型语言模型 (矿-R1) 快速识别有效的矿太阳能电池添加剂.
- 为了研究被识别的添加剂,乙基2-氨基酸化物 (EAH) 提供的性能和稳定性增强.
主要方法:
- 利用佩罗夫斯基特-R1大型语言模型进行添加式发现.
- 在矿太阳能电池设备中合成并纳入乙烯-2-氨基酸化物 (EAH).
- 设备性能特征,包括功率转换效率 (PCE) 和长期稳定性.
主要成果:
- 确定了乙基2-氨基酸化物 (EAH) 作为一种高效的添加剂.
- 实现了22.58%的冠军功率转换效率 (PCE).
- 在N2中经过1368小时后保持95.1%的PCE,在65°C下经过1272小时后保持94.1%.
结论:
- 乙基2-氨基酸化物 (EAH) 有效地使缺陷无效,并调节矿太阳能电池中的结晶.
- 人工智能驱动的材料设计加速了高性能,稳定的矿光电子材料的发现.
- 这种方法对开发可持续的矿太阳能电池技术具有前景.
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