Langner Stefan, Häse Florian, Perea José Darío, Stubhan Tobias, Hauch Jens, Roch Loïc M, Heumueller Thomas, Aspuru-Guzik Alán, Brabec Christoph J
Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Martensstrasse 7, Erlangen, 91058, Germany.
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA.
Adv Mater. 2020 Apr;32(14):e1907801. doi: 10.1002/adma.201907801. Epub 2020 Feb 12.
Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.
通过设计三元共混物实现了提高有机光伏（OPV）效率和稳定性的根本性进展，这代表了多组分活性层共混物的明显发展趋势。本文报道了高通量和自主实验方法的发展，用于有效优化用于OPV的多组分聚合物共混物。介绍了一种每天能够制备多达6048个薄膜的自动成膜方法。为这个自动化实验平台配备贝叶斯优化算法，构建了一个自动驾驶实验室，该实验室能够自主评估测量结果以设计和执行下一个实验。为了证明这些方法的潜力，绘制并优化了用于光稳定性的四元OPV共混物的4D参数空间。使用传统方法大约需要100毫克材料，而基于机器人的平台可以用不到10毫克的材料筛选2000种组合，并且通过机器学习实现的自主实验能够识别出用量不到1毫克的稳定成分。
