Conducting polymer thin film for optoelectronic devices applications

Abdel‐Aziz, M.H.; Zwawi, Mohammed; Al‐Hossainy, Ahmed F.; Zoromba, M. Sh.

doi:10.1002/pat.5290

Cited by 17 publications

(2 citation statements)

References 66 publications

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“…The CGMD/ML coupling can also predict optical properties of conjugated polymers, for example, UV-vis (light absorption) spectrum, one of the key experimental methods for characterization of conjugated polymers ( Ivan et al, 2012 ; Root et al, 2017 ; Abdel‐Aziz et al, 2021 ). However, there is no well-established bridge between these CG polymer structures and their absorption spectroscopy.…”

Section: Application Of ML For Understanding and Design Of Polymer Chainsmentioning

confidence: 99%

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

2022

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In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics—polymeric configuration characterization, feed-forward property prediction, and inverse design—in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

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Section: Application Of ML For Understanding and Design Of Polymer Chainsmentioning

confidence: 99%

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

2022

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“…Conducting polymers (CP) have been studied extensively during the last two decades because of their tunable electrical conductivity and high electroactive character. The potential of CPs has been studied primarily by considering them as dry materials to determine their applications in microelectronics, [1][2][3] EMI shielding, 4,5 optoelectronics, 6,7 photonics, 8 solar cells, 9,10 tissue engineering, 11,12 electrocatalysts, 13,14 and so on. Another major class of applications of CPs is based on their compositiondependent properties.…”

Section: Introductionmentioning

confidence: 99%