Low-gap conjugated polymers have enabled an impressive increase in the efficiencies of organic solar cells, primarily due to their red absorption which allows harvesting of that part of the solar spectrum. Here, we report that the true optical gap of one prototypical material, PTB7, is in fact at significantly higher energy than has previously been reported, indicating that the red absorption utilized in these materials in solar cells is entirely due to chain aggregation. Using single-molecule spectroscopy we find that PL from isolated nanoscale aggregates consists of multiple independently emitting chromophores. At the single-molecule level, however, straight single chains with a high degree of emission polarization are observed. The PL is found to be ~0.4 eV higher in energy, with a longer lifetime than the red aggregates, and is attributed to single chromophores. Our findings indicate that the impressive light-harvesting abilities of PTB7 in the red spectral region arises solely from chain aggregation.
Rapid Computational Optimization of Molecular Properties using Genetic Algorithms: Searching Across Millions of Compounds for Organic Photovoltaic Materials
Electronic structure and non-linear optical properties of organic photovoltaic systems with potential applications on solar cell devices: A DFT approach
Delocalisation explains efficient transport and charge generation in neat Y6 organic photovoltaics
University of Sydney（悉尼大学）；Victoria University of Wellington（惠灵顿维多利亚大学）；MacDiarmid Institute for Advanced Materials and Nanotechnology（先进材料与纳米技术麦克迪亚米德研究所）；The Dodd-Walls Centre for Photonic and Quantum Technologies（光子与量子技术多兹中心）；Institute for Integrated Cell-Material Sciences (iCeMS)（细胞材料综合科学研究所）
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校西贝尔计算与数据科学学院）；Department of Chemical & Biomolecular Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校化学与生物分子工程系）
Temperature-Driven Sequential Modeling for the Prediction of Annual Power Conversion Efficiency Profiles of Organic Photovoltaic Materials: Douala Case Study
University of Douala（杜阿拉大学）；Stellenbosch University（斯泰伦博斯大学）；University of Quebec in Abitibi-Témiscamingue（阿比蒂比-蒂米斯坎明格魁北克大学）；University of Yaounde 1（雅温得第一大学）
Machine Learning for Screening Large Organic Molecules
有机半导体是廉价、可扩展和可持续的电子器件、发光二极管和光电子器件的有前途的材料。对于有机光电池来说，在广阔的化合物空间中寻找具有合适性质的化合物是一个挑战。例如，电离能应该适合太阳光的光谱，并且能级必须允许有效的电荷传输。在这里，开发了一种机器学习模型，用于快速准确地估计给定分子结构的HOMO和LUMO能量。它建立在SchNet模型（Schütt et al.（2018））的基础上，并增加了“Set 2Set”读出模块（Vinyals et al.（2016））。Set 2Set模块比求和和平均聚合具有更强的表达能力，更适合于所考虑的复杂量。大多数以前的模型都是在相当小的分子上训练和评估的。因此，第二个贡献是通过添加来自其他来源的更大分子并建立一致的训练/验证/测试分割来扩展机器学习方法的范围。作为第三个贡献，我们做了一个多任务分析，以解决来自不同理论层次的不同来源的问题。所有这三个贡献结合起来，使模型的准确性接近化学准确性。
