利用自动化研究平台和机器学习揭示有机太阳能电池的加工稳定性状况

IF 22.7 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Infomat Pub Date : 2024-06-14 DOI:10.1002/inf2.12554
Xiaoyan Du, Larry Lüer, Thomas Heumueller, Andrej Classen, Chao Liu, Christian Berger, Jerrit Wagner, Vincent M. Le Corre, Jiamin Cao, Zuo Xiao, Liming Ding, Karen Forberich, Ning Li, Jens Hauch, Christoph J. Brabec
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引用次数: 0

摘要

我们使用自动化研究平台,结合机器学习,评估和了解 40 多种供体和受体组合的有机光伏(OPV)器件在生产过程中对空气和光线的适应能力。该平台的标准化规程和高度可重复性产生了一个种类繁多且真实可信的数据集,用于部署机器学习模型,以了解稳定性与化学、能量和形态结构之间的联系。我们发现,在生产过程中,有效间隙 Eg,eff 是预测空气/光适应性的最强指标,它表明在加工条件下降解的主要因素是单线态氧而不是超氧阴离子。如果只考虑化学结构的特征,也就是在任何实验之前就可以获得的信息,也可以对耐空气/耐光性进行类似的良好预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning

Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning

Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning

We use an automated research platform combined with machine learning to assess and understand the resilience against air and light during production of organic photovoltaic (OPV) devices from over 40 donor and acceptor combinations. The standardized protocol and high reproducibility of the platform results in a dataset of high variety and veracity to deploy machine learning models to encounter links between stability and chemical, energetic, and morphological structure. We find that the strongest predictor for air/light resilience during production is the effective gap Eg,eff which points to singlet oxygen rather than the superoxide anion being the dominant agent in degradation under processing conditions. A similarly good prediction of air/light resilience can also be achieved by considering only features from chemical structure, that is, information which is available prior to any experimentation.

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来源期刊
Infomat
Infomat MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
37.70
自引率
3.10%
发文量
111
审稿时长
8 weeks
期刊介绍: InfoMat, an interdisciplinary and open-access journal, caters to the growing scientific interest in novel materials with unique electrical, optical, and magnetic properties, focusing on their applications in the rapid advancement of information technology. The journal serves as a high-quality platform for researchers across diverse scientific areas to share their findings, critical opinions, and foster collaboration between the materials science and information technology communities.
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