Aggregation of a Dibenzo[b,def]chrysene Based Organic Photovoltaic Material in Solution

2014 ◽  
Vol 118 (24) ◽  
pp. 6839-6849 ◽  
Author(s):  
Alexandr N. Simonov ◽  
Peter Kemppinen ◽  
Cristina Pozo-Gonzalo ◽  
John F. Boas ◽  
Ante Bilic ◽  
...  

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.



2008 ◽  
Vol 112 (10) ◽  
pp. 3926-3934 ◽  
Author(s):  
Larry W. Barbour ◽  
Ryan D. Pensack ◽  
Maureen Hegadorn ◽  
Sergei Arzhantsev ◽  
John B. Asbury


2020 ◽  
Author(s):  
Jaebeom Han ◽  
Huseyin Aksu ◽  
Buddhadev Maiti ◽  
Xiang Sun ◽  
Eitan Geva ◽  
...  


2013 ◽  
Vol 14 (5) ◽  
pp. 1242-1248 ◽  
Author(s):  
G. Volonakis ◽  
L. Tsetseris ◽  
S. Logothetidis


Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.



Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.



2017 ◽  
Vol 9 (17) ◽  
pp. 14945-14952 ◽  
Author(s):  
Yun Long ◽  
Gordon J. Hedley ◽  
Arvydas Ruseckas ◽  
Mithun Chowdhury ◽  
Thomas Roland ◽  
...  


2020 ◽  
Author(s):  
Jaebeom Han ◽  
Huseyin Aksu ◽  
Buddhadev Maiti ◽  
Xiang Sun ◽  
Eitan Geva ◽  
...  


2020 ◽  
Author(s):  
Jaebeom Han ◽  
Huseyin Aksu ◽  
Buddhadev Maiti ◽  
Xiang Sun ◽  
Eitan Geva ◽  
...  


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