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<p>Poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives have emerged as critical materials in modern electronic applications due to their exceptional electrical conductivity, biocompatibility, and processability. This comprehensive review examines the state-of-the-art computational strategies employed for designing and optimizing PEDOT-based electrodes across various electronic applications. We systematically assess multiscale modeling approaches, from quantum mechanical calculations to macroscopic device simulations, including Density Functional Theory (DFT), atomistic and coarse-grained Molecular Dynamics (MD), Finite Element Method (FEM), and emerging Machine Learning/Artificial Intelligence techniques. The review elucidates how these computational methods provide critical insights into PEDOT’s electronic structure, charge transport mechanisms, morphological characteristics, and interfacial behaviors. Particular emphasis is placed on structure-property relationships, including the aromatic-to-quinoid transition upon doping, the formation of polarons and bipolarons, the influence of π-π stacking on charge mobility, and the critical role of counterions in modulating electronic performance. We demonstrate their application in designing optimized electrodes for supercapacitors, organic electrochemical transistors, and flexible electronics. Finally, we analyze existing limitations in current computational frameworks and identify promising future directions, including multiscale integration, improved force fields, and quantum machine learning, that will accelerate the rational design of next-generation PEDOT-based materials with tailored functionalities.</p> Graphical Abstract <p>This review highlights the integration of computational strategies like DFT, MD, FEM, and ML/AI in designing PEDOT-based electrodes, emphasizing their role in understanding electronic structure, charge transport, morphology, and mechanical behavior. It explores recent advancements, applications, limitations, and future directions for optimizing PEDOT electrodes in electronic devices.</p> <p></p>

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Computational strategies for designing PEDOT-Based electrodes in electronic applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Gbolahan Joseph Adekoya,
  • Oluwasegun Chijioke Adekoya,
  • Mpho Muloiwa,
  • Emmanuel Rotimi Sadiku,
  • Williams Kehinde Kupolati,
  • Yskandar Hamam

摘要

Poly(3,4-ethylenedioxythiophene) (PEDOT) and its derivatives have emerged as critical materials in modern electronic applications due to their exceptional electrical conductivity, biocompatibility, and processability. This comprehensive review examines the state-of-the-art computational strategies employed for designing and optimizing PEDOT-based electrodes across various electronic applications. We systematically assess multiscale modeling approaches, from quantum mechanical calculations to macroscopic device simulations, including Density Functional Theory (DFT), atomistic and coarse-grained Molecular Dynamics (MD), Finite Element Method (FEM), and emerging Machine Learning/Artificial Intelligence techniques. The review elucidates how these computational methods provide critical insights into PEDOT’s electronic structure, charge transport mechanisms, morphological characteristics, and interfacial behaviors. Particular emphasis is placed on structure-property relationships, including the aromatic-to-quinoid transition upon doping, the formation of polarons and bipolarons, the influence of π-π stacking on charge mobility, and the critical role of counterions in modulating electronic performance. We demonstrate their application in designing optimized electrodes for supercapacitors, organic electrochemical transistors, and flexible electronics. Finally, we analyze existing limitations in current computational frameworks and identify promising future directions, including multiscale integration, improved force fields, and quantum machine learning, that will accelerate the rational design of next-generation PEDOT-based materials with tailored functionalities.

Graphical Abstract

This review highlights the integration of computational strategies like DFT, MD, FEM, and ML/AI in designing PEDOT-based electrodes, emphasizing their role in understanding electronic structure, charge transport, morphology, and mechanical behavior. It explores recent advancements, applications, limitations, and future directions for optimizing PEDOT electrodes in electronic devices.