High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

Omar, Ömer H.; Cueto, Marcos del; Nematiaram, Tahereh; Troisi, Alessandro

doi:10.1039/d1tc03256a

Cited by 29 publications

(29 citation statements)

References 441 publications

(469 reference statements)

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“…9,36,44 Due to the gulf between the scale of CSP that is currently affordable and the size of chemical space, more targeted methods, such as data-driven techniques, are required to focus effort on the best candidate molecules. 45 Data-driven methods to generate molecules, which have mainly been applied in the area of drug discovery, have also been demonstrated for functional materials discovery.…”

Section: Chemical Space Explorationmentioning

confidence: 99%

“…Quantitative structure-property relationship and ML models for property prediction have been applied as steps in the funneling strategy. 45,50 HTVS can use generative models or existing chemical databases, such as ZINC, 51 the Cambridge Structural Database, 52,53 and the Harvard Clean Energy Project, 54 to build the initial populations of compounds to screen. Although CSP has not been applied in truly high-throughput studies, improvements in efficiency of the methods have made it possible to perform CSP, followed by property prediction for the sets of low-energy predicted crystal structures, for up to about 30 small molecules.…”

Section: High-throughput Virtual Screeningmentioning

confidence: 99%

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Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

et al. 2022

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The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This article discusses the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property. Graphical abstract

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Section: Chemical Space Explorationmentioning

confidence: 99%

Section: High-throughput Virtual Screeningmentioning

confidence: 99%

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

et al. 2022

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“…In high-throughput virtual screening (HTVS) studies, it has therefore become common to focus on simplied descriptors that are known to correlate with the property of interest. [11][12][13] Such descriptors include, e.g., the binding energy of a key intermediate in catalysis or the internal reorganization energy (l) in molecular electronics.…”

Section: Introductionmentioning

confidence: 99%

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

et al. 2022

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“…While organic electronics have been widely studied through computational methods, − computation of their dielectric properties has not been thoroughly explored. Electronic structure calculations, while accurate, are only computationally feasible for the computation of the electronic dielectric constant.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Enhancing the Dielectric Constant of Organic Materials

et al. 2022

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High dielectric constant organic semiconductors, often obtained by the use of ethylene glycol (EG) side chains, have gained attention in recent years in the efforts of improving the device performance for various applications. Dielectric constant enhancements due to EGs have been demonstrated extensively, but various effects, such as the choice of the particular molecule and the frequency and temperature regime, that determine the extent of this enhancement require further understanding. In this work, we study these effects by means of polarizable molecular dynamics simulations on a carefully selected set of fullerene derivatives with EG side chains. The selection allows studying the dielectric response in terms of both the number and length of EG chains and also the choice of the group connecting the fullerene to the EG chain. The computed time- and frequency-dependent dielectric responses reveal that the experimentally observed rise of the dielectric constant within the kilo/megahertz regime for some molecules is likely due to the highly stretched dielectric response of the EGs: the initial sharp increase over the first few nanoseconds is followed by a smaller but persistent increase in the range of microseconds. Additionally, our computational protocol allows the separation of different factors that contribute to the overall dielectric constant, providing insights to make several molecular design guides for future organic materials in order to enhance their dielectric constant further.

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High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

Abstract: We present a review of the field of high throughput virtual screening for organic electronics materials focusing on the sequence of methodological choices that determine each virtual screening protocol. These...

Cited by 29 publications

References 441 publications

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

Strategies for Enhancing the Dielectric Constant of Organic Materials

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