空间统一驱动的贝叶斯优化,用于快速开发印刷矿太阳能电池
Julia E Huddy1, Yanan Li1, Mariia Klymenko1
1Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
Small (Weinheim an der Bergstrasse, Germany)
|November 26, 2025
概括
贝叶斯优化通过专注于空间统一性来改善印刷矿太阳能电池. 这种机器学习方法可以提高大面积设备的性能和制造效率.
科学领域:
- 材料科学 材料科学 材料科学
- 光电学是指光电子产品.
- 机器学习应用 机器学习应用
背景情况:
- 打印的金属化物矿可为光电子产品 (如太阳能电池) 提供具有成本效益的高通量制造.
- 矿薄膜的空间异质性阻碍了大型设备的性能,尽管目前的模式能力.
研究的目的:
- 以空间统一驱动的贝叶斯优化 (BO) 方法加速印刷矿太阳能电池的开发.
- 通过减轻空间异质性来提高大面积矿太阳能电池的性能.
主要方法:
- 利用贝叶斯优化 (BO) 代用模型探索墨水化学和印刷物理的6D设计空间.
- 利用循序渐进的实验 (约100次试验),以客观函数为指导,测量空间光发光 (PL) 差异.
- 进行了油墨配方的风学比较,以解决萨夫曼-泰勒文物并提高薄膜的统一性.
主要成果:
- 优化统一性显著改善了光伏性能.
- 在小面积 (0.134厘米2) 的设备中实现了约20%的功率转换效率 (PCE).
- 在大面积 (1厘米2) 的设备中证明了>16%的PCE,展示了可扩展性.
结论:
- 统一性驱动的BO是一种优化印刷矿太阳能电池的高效方法.
- 这种方法有效地识别了关键的打印物理,并减轻了可扩展设备的空间异质性.
- 机器学习策略加速了基于矿的光电子产品的开发,并提高了其性能.
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