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 Evolutionary Chemical Space Exploration for Functional Materials: Computational Organic Semiconductor Discovery

2020 ◽  
Author(s):  
Chi Y. Cheng ◽  
Josh E. Campbell ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Chemical Space ◽  
Functional Materials ◽  
Building Blocks ◽  
Charge Carrier Mobility ◽  
High Electron ◽  
Large Space ◽  
Structure And Property

Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobility, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ca.1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

scholarly journals Evolutionary Chemical Space Exploration for Functional Materials: Computational Organic Semiconductor Discovery

2020 ◽  
Author(s):  
Chi Y. Cheng ◽  
Josh E. Campbell ◽  
Graeme Day
Keyword(s):  
Crystal Structure ◽  
Organic Semiconductors ◽  
Structure Prediction ◽  
Chemical Space ◽  
Functional Materials ◽  
Building Blocks ◽  
Charge Carrier Mobility ◽  
High Electron ◽  
Large Space ◽  
Structure And Property

Computational methods, including crystal structure and property prediction, have the potential to accelerate the materials discovery process by enabling structure prediction and screening of possible molecular building blocks prior to their synthesis. However, the discovery of new functional molecular materials is still limited by the need to identify promising molecules from a vast chemical space. We describe an evolutionary method which explores a user specified region of chemical space to identify promising molecules, which are subsequently evaluated using crystal structure prediction. We demonstrate the methods for the exploration of aza-substituted pentacenes with the aim of finding small molecule organic semiconductors with high charge carrier mobility, where the space of possible substitution patterns is too large to exhaustively search using a high throughput approach. The method efficiently explores this large space, typically requiring calculations on only ca.1% of molecules during a search. The results reveal two promising structural motifs: aza-substituted naphtho[1,2-a]anthracenes with reorganisation energies as low as pentacene and a series of pyridazine-based molecules having both low reorganisation energies and high electron affinities.

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 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.

scholarly journals Accelerated crystal structure prediction of multi-elements random alloy using expandable features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taewon Jin ◽  
Ina Park ◽  
Taesu Park ◽  
Jaesik Park ◽  
Ji Hoon Shim
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Structure Prediction ◽  
Large Scale ◽  
Computational Cost ◽  
Functional Materials ◽  
Training Dataset ◽  
High Entropy Alloy ◽  
Crystal Structure Prediction ◽  
Data Set

AbstractProperties of solid-state materials depend on their crystal structures. In solid solution high entropy alloy (HEA), its mechanical properties such as strength and ductility depend on its phase. Therefore, the crystal structure prediction should be preceded to find new functional materials. Recently, the machine learning-based approach has been successfully applied to the prediction of structural phases. However, since about 80% of the data set is used as a training set in machine learning, it is well known that it requires vast cost for preparing a dataset of multi-element alloy as training. In this work, we develop an efficient approach to predicting the multi-element alloys' structural phases without preparing a large scale of the training dataset. We demonstrate that our method trained from binary alloy dataset can be applied to the multi-element alloys' crystal structure prediction by designing a transformation module from raw features to expandable form. Surprisingly, without involving the multi-element alloys in the training process, we obtain an accuracy, 80.56% for the phase of the multi-element alloy and 84.20% accuracy for the phase of HEA. It is comparable with the previous machine learning results. Besides, our approach saves at least three orders of magnitude computational cost for HEA by employing expandable features. We suggest that this accelerated approach can be applied to predicting various structural properties of multi-elements alloys that do not exist in the current structural database.

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
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