Computational Modeling of Polymer Semiconductors for Electronic Applications

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 03 | Page :
    By

    Ruchi Sharma,

  • Nitu Sehrawat,

  • Anil Kumar,

  • Suman Yadav,

  • Annu Dabas,

  • Karunapati Tripathi,

  1. Associate Professor, Department of Electronics and Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Associate Professor, Department of Applied Science, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  3. Associate Professor, Department of Applied Science, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Associate Professor, Department of Electronics and Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  5. Assistant Professor, Department of Electronics and Communication Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  6. Assistant Professor, Department of Applied Science, Maharaja Agrasen Institute of Technology, Sector-22, Rohini, Delhi, India

Abstract

Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer semiconductors and explains their role in understanding structure-property relationships. Density functional theory is useful for estimating frontier orbital energies, band gaps, charge-transfer parameters, and optical transitions. Molecular dynamics simulation helps in examining chain conformation, intermolecular packing, and thin-film morphology, all of which strongly influence charge transport. Kinetic Monte Carlo methods and related transport models are important for predicting carrier mobility and transport pathways in disordered and semicrystalline systems. In addition, machine learning methods are increasingly applied for rapid screening of candidate materials and for accelerating polymer design. The study highlights that no single computational method is sufficient to explain the full electronic behavior of polymer semiconductors. Instead, meaningful prediction requires an integrated multiscale framework that connects molecular-level electronic structure with morphology and device-level charge transport. Overall, computational modeling has become an essential tool for the rational design of efficient, stable, and application-oriented polymer semiconductor materials.

Keywords: Polymer semiconductors, computational modeling, charge transport, organic electronics, density functional theory, molecular dynamics

How to cite this article:
Ruchi Sharma, Nitu Sehrawat, Anil Kumar, Suman Yadav, Annu Dabas, Karunapati Tripathi. Computational Modeling of Polymer Semiconductors for Electronic Applications. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Ruchi Sharma, Nitu Sehrawat, Anil Kumar, Suman Yadav, Annu Dabas, Karunapati Tripathi. Computational Modeling of Polymer Semiconductors for Electronic Applications. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=244548


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Ahead of Print Subscription Original Research
Volume 14
03
Received 16/04/2025
Accepted 15/05/2026
Published 21/05/2026
Publication Time 400 Days
1

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