弹性(材料科学)
多样性(控制论)
有机太阳能电池
计算机科学
降级(电信)
人工智能
理论(学习稳定性)
机器学习
单线态氧
光伏系统
环境科学
材料科学
化学
氧气
工程类
电信
电气工程
有机化学
复合材料
作者
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
出处
期刊:InfoMat
[Wiley]
日期:2024-06-14
卷期号:6 (7)
被引量:1
摘要
Abstract 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 E g,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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