Dicyanovinyl-Substituted Oligothiophenes: Structure-Property Relationships and Application in Vacuum-Processed Small Molecule Organic Solar Cells

2011 ◽  
Vol 21 (5) ◽  
pp. 897-910 ◽  
Author(s):  
Roland Fitzner ◽  
Egon Reinold ◽  
Amaresh Mishra ◽  
Elena Mena-Osteritz ◽  
Hannah Ziehlke ◽  
...  
2018 ◽  
Vol 10 (42) ◽  
pp. 36037-36046 ◽  
Author(s):  
Jisu Hong ◽  
Min Jae Sung ◽  
Hyojung Cha ◽  
Chan Eon Park ◽  
James R. Durrant ◽  
...  

Author(s):  
Keli Shi ◽  
Beibei Qiu ◽  
Can Zhu ◽  
Xinxin Xia ◽  
Xiaonan Xue ◽  
...  

To deeply investigate the structure-property relationship in organic solar cells (OSCs), one widely used strategy is to design a series of organic photovoltaic materials with same chemical formular but different...


ChemSusChem ◽  
2015 ◽  
Vol 8 (9) ◽  
pp. 1496-1496
Author(s):  
Yu Jin Kim ◽  
Eun Soo Ahn ◽  
Sang Hun Jang ◽  
Tae Kyu An ◽  
Soon-Ki Kwon ◽  
...  

2017 ◽  
Vol 5 (19) ◽  
pp. 9217-9232 ◽  
Author(s):  
Stephen Loser ◽  
Sylvia J. Lou ◽  
Brett M. Savoie ◽  
Carson J. Bruns ◽  
Amod Timalsina ◽  
...  

Understanding the effects of molecular shape on active layer charge transport in OPVs.


ChemSusChem ◽  
2015 ◽  
Vol 8 (9) ◽  
pp. 1548-1556 ◽  
Author(s):  
Yu Jin Kim ◽  
Eun Soo Ahn ◽  
Sang Hun Jang ◽  
Tae Kyu An ◽  
Soon-Ki Kwon ◽  
...  

2021 ◽  
Author(s):  
Yaping Wen ◽  
Bohan Yan ◽  
Theophile Gaudin ◽  
Jing Ma ◽  
Haibo Ma

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>


2021 ◽  
Author(s):  
Yaping Wen ◽  
Bohan Yan ◽  
Theophile Gaudin ◽  
Jing Ma ◽  
Haibo Ma

<p><a></a><a>In addition to designing new donor (D) and/or acceptor (A) molecules, the optimization of</a><a></a><a> experimental fabrication conditions </a>for the organic solar cells (OSCs) is also a complex, multidimensional challenge, which hasn’t been theoretically explored. Herein, a new framework for simultaneous optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationships (QSPR) model built by machine learning. Combining the <a></a><a>device parameters</a> with<a></a><a> structural and electronic </a>variables, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a huge chemical space containing <a>1,942,785</a> D/A pairs is explored to find potential synergistic ones. Favorable expereimental parameters such as root-mean-square (<i>RMS</i>) and the D/A ratio (<i>DAratio</i>) are further screened by grid search methods. <a></a><a></a><a></a><a>Overall, this study suggests </a>the feasibility to optimize D/A molecule pairs and device specifications simultaneously by enabling better-informed and data-driven techniques and this could facilitate the acceleration of improving OSCs efficiencies.</p>


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