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Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties
Trends in Chemistry ( IF 15.7 ) Pub Date : 2020-04-25 , DOI: 10.1016/j.trechm.2020.03.006
Tonghui Wang , Grit Kupgan , Jean-Luc Brédas

Substantial enhancements in the efficiencies of bulk-heterojunction (BHJ) organic solar cells (OSCs) have come from largely trial-and-error-based optimizations of the morphology of the active layers. Further improvements, however, require a detailed understanding of the relationships among chemical structure, morphology, electronic properties, and device performance. On the experimental side, characterization of the local (i.e., nanoscale) morphology remains challenging, which has called for the development of robust computational methodologies that can reliably address those aspects. In this review, we describe how a methodology that combines all-atom molecular dynamics (AA-MD) simulations with density functional theory (DFT) calculations allows the establishment of chemical structure–local morphology–electronic properties relationships. We also provide a brief overview of coarse-graining methods in an effort to bridge local to global (i.e., mesoscale to microscale) morphology. Finally, we give a few examples of machine learning (ML) applications that can assist in the discovery of these relationships.



中文翻译:

有机光伏:与化学结构,局部形态和电子特性有关

本体异质结(BHJ)有机太阳能电池(OSC)效率的显着提高来自有源层形态的很大程度上基于试验和错误的优化。然而,进一步的改进需要详细了解化学结构,形态,电子特性和器件性能之间的关系。在实验方面,局部(即纳米级)形态的表征仍然具有挑战性,这要求开发能够可靠地解决这些方面的强大计算方法。在这篇综述中,我们描述了将全原子分子动力学(AA-MD)模拟与密度泛函理论(DFT)计算相结合的方法如何建立化学结构-局部形态-电子性质之间的关系。我们还提供了粗粒度方法的简要概述,以尝试将局部形态转换为全局形态(从中尺度到微观尺度)。最后,我们给出了一些可以帮助发现这些关系的机器学习(ML)应用程序的示例。

更新日期:2020-04-25
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