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.

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 Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

scholarly journals Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

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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