Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems
复制标题
DOI：
10.1002/adma.201907801
复制
发表时间：
2020-02-12
影响因子：
29.4
通讯作者：
Brabec, Christoph J.
中科院分区：
文献类型：
--
作者：
Langner, Stefan;Hase, Florian;Brabec, Christoph J.
通过设计三元共混物来提高有机光伏(OPV)的效率和稳定性是一种根本性的进展，这代表了多组分有源层混合的明显趋势。报道了高通量和自主实验方法的发展，以有效地优化用于OPV的多组分聚合物共混物。介绍了一种能够每天制造多达6048个薄膜的自动成膜方法。为这个自动化实验平台配备了贝叶斯优化，构建了一个自动驾驶实验室，它自动评估测量结果，以设计和执行下一步的实验。为了展示这些方法的潜力，我们绘制了四元OPV共混物的4D参数空间，并对其进行了光稳定性优化。使用传统方法，大约需要100毫克的材料，而基于机器人的平台可以筛选2000种不到10毫克的组合，而支持机器学习的自主实验识别出不到1毫克的稳定成分。
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.