Relationship Between the Crystal Structures and Transistor Performance of Organic Semiconductors

2015 ◽  
pp. 607-625
Author(s):  
Yoshiro Yamashita
Keyword(s):  
Crystal Structures ◽  
Organic Semiconductors

Gold‐Catalyzed Facile Synthesis and Crystal Structures of Benzene‐/Naphthalene‐Based Bispentalenes as Organic Semiconductors

2018 ◽  
Vol 25 (1) ◽  
pp. 216-220 ◽  
Author(s):  
Kohei Sekine ◽  
Jürgen Schulmeister ◽  
Fabian Paulus ◽  
Katelyn P. Goetz ◽  
Frank Rominger ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Organic Semiconductors ◽  
Facile Synthesis

Influences of Crystal Structures and Molecular Sizes on the Charge Mobility of Organic Semiconductors: Oligothiophenes

2008 ◽  
Vol 20 (9) ◽  
pp. 3205-3211 ◽  
Author(s):  
Xiaodi Yang ◽  
Linjun Wang ◽  
Caili Wang ◽  
Wei Long ◽  
Zhigang Shuai
Keyword(s):  
Crystal Structures ◽  
Organic Semiconductors ◽  
Charge Mobility ◽  
Molecular Sizes

scholarly journals Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

2018 ◽  
Author(s):  
Jack Yang ◽  
Sandip De ◽  
Joshua E Campbell ◽  
Sean Li ◽  
Michele Ceriotti ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Crystal Structure Prediction ◽  
Structural Isomers ◽  
Computational Screening ◽  
Learning Techniques

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, <b>1</b>) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of <b>1</b> to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to <b>1</b> and suggest two molecules in particular that represent attractive synthetic targets.

scholarly journals Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

2018 ◽  
Author(s):  
Jack Yang ◽  
Sandip De ◽  
Joshua E Campbell ◽  
Sean Li ◽  
Michele Ceriotti ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Crystal Structure Prediction ◽  
Structural Isomers ◽  
Computational Screening ◽  
Learning Techniques

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, <b>1</b>) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of <b>1</b> to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to <b>1</b> and suggest two molecules in particular that represent attractive synthetic targets.

scholarly journals Large–Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

2018 ◽  
Author(s):  
Jack Yang ◽  
Sandip De ◽  
Joshua E Campbell ◽  
Sean Li ◽  
Michele Ceriotti ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Crystal Structures ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Crystal Structure Prediction ◽  
Structural Isomers ◽  
Computational Screening ◽  
Learning Techniques

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole azaphenacene molecule (pyrido[2,3-b]pyrido[3`,2`:4,5]-pyrrolo[3,2-g]indole, <b>1</b>) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of <b>1</b> to assess charge mobility in their predicted crystal structures. Machine--learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to <b>1</b> and suggest two molecules in particular that represent attractive synthetic targets.

Computational investigation of hole mobilities in organic semiconductors: comparison of single crystal structures and surface adsorbed clusters

2014 ◽  
Vol 174 ◽  
pp. 281-296 ◽  
Author(s):  
Rose A. Krawczuk ◽  
Steven Tierney ◽  
William Mitchell ◽  
Joseph J. W. McDouall
Keyword(s):  
Thin Film ◽  
Surface Morphology ◽  
Single Crystal ◽  
Crystal Structures ◽  
Organic Semiconductors ◽  
Experimental Information ◽  
Computational Investigation ◽  
Model Surface ◽  
Organic Monolayer ◽  
Single Crystal Structures

We report hole mobilities obtained computationally based on both single crystal geometries and those obtained from crystal fragments optimised on a model surface. Such computational estimates can differ considerably from experimentally measured thin film mobilities. One source of this discrepancy is due to a difference in the morphology of the thin film compared with that of the crystal. Here, predictions of thin film hole mobilities based on optimised structures are given. A model surface is used to provide an inert geometric platform for the formation of an organic monolayer. The model is tested on pentacene and TIPS-pentacene for which experimental information of the surface morphology exists. The model has also been applied to four previously uninvestigated structures. Two of the compounds studied had fairly low predicted mobilities in their single crystal structures, which were vastly improved post-optimisation. This is in accord with experiment.

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