Rational design and crystal structure prediction of ring-fused double-PDI compounds as n-channel organic semiconductors: a DFT study

2021 ◽  
Author(s):  
Suryakanti Debata ◽  
Smruti R. Sahoo ◽  
Rudranarayan Khatua ◽  
Sridhar Sahu
Keyword(s):  
Crystal Structure ◽  
Charge Transport ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Rational Design ◽  
Molecular Design ◽  
Design Strategy ◽  
Dft Study ◽  
Crystal Structure Prediction ◽  
Model Compounds

In this study, we present an effective molecular design strategy to develop the n-type charge transport characteristics in organic semiconductors, using ring-fused double perylene diimides (DPDIs) as the model compounds.

scholarly journals Evolutionary chemical space exploration for functional materials: computational organic semiconductor discovery

2020 ◽  
Vol 11 (19) ◽  
pp. 4922-4933 ◽  
Author(s):  
Chi Y. Cheng ◽  
Josh E. Campbell ◽  
Graeme M. Day
Keyword(s):  
Crystal Structure ◽  
Organic Semiconductors ◽  
Organic Semiconductor ◽  
Structure Prediction ◽  
Chemical Space ◽  
Space Exploration ◽  
Functional Materials ◽  
Crystal Structure Prediction ◽  
Evolutionary Optimisation

Evolutionary optimisation and crystal structure prediction are used to explore chemical space for molecular organic semiconductors.

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

2018 ◽  
Vol 30 (13) ◽  
pp. 4361-4371 ◽  
Author(s):  
Jack Yang ◽  
Sandip De ◽  
Josh E. Campbell ◽  
Sean Li ◽  
Michele Ceriotti ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Large Scale ◽  
Crystal Structure Prediction ◽  
Computational Screening

scholarly journals Predicted energy–structure–function maps for the evaluation of small molecule organic semiconductors

2017 ◽  
Vol 5 (30) ◽  
pp. 7574-7584 ◽  
Author(s):  
Josh E. Campbell ◽  
Jack Yang ◽  
Graeme M. Day
Keyword(s):  
Crystal Structure ◽  
Structure Function ◽  
Organic Semiconductors ◽  
Small Molecule ◽  
Structure Prediction ◽  
Energy Structure ◽  
Crystal Structure Prediction

Crystal structure prediction is used to calculate energy–structure–function maps of the charge mobilities in molecular organic semiconductors.

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.

Crystal Structure Prediction via Deep Learning

2018 ◽  
Vol 140 (32) ◽  
pp. 10158-10168 ◽  
Author(s):  
Kevin Ryan ◽  
Jeff Lengyel ◽  
Michael Shatruk
Keyword(s):  
Crystal Structure ◽  
Deep Learning ◽  
Structure Prediction ◽  
Crystal Structure Prediction

scholarly journals New high-pressure phases of Fe7N3 and Fe7C3 stable at Earth's core conditions: evidences for carbon–nitrogen isomorphism in Fe-compounds

RSC Advances ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 3577-3581 ◽  
Author(s):  
Nursultan Sagatov ◽  
Pavel N. Gavryushkin ◽  
Talgat M. Inerbaev ◽  
Konstantin D. Litasov
Keyword(s):  
Crystal Structure ◽  
High Pressure ◽  
Ab Initio ◽  
Structure Prediction ◽  
Harmonic Approximation ◽  
Crystal Structure Prediction ◽  
Earth’S Core ◽  
Earth's Core ◽  
Core Conditions

We carried out ab initio calculations on the crystal structure prediction and determination of P–T diagrams within the quasi-harmonic approximation for Fe7N3 and Fe7C3.

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