Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

Vinayak Bhat, Connor P. Callaway, Chad Risko

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Original languageEnglish
Pages (from-to)7498-7547
Number of pages50
JournalChemical Reviews
Volume123
Issue number12
DOIs
StatePublished - Jun 28 2023

Bibliographical note

Funding Information:
This work was funded in part by the National Science Foundation through award numbers DMR 1627428 and DMR 1922174 and by the Office of Naval Research through award number N00014-22-1-2179.

Publisher Copyright:
© 2023 American Chemical Society.

ASJC Scopus subject areas

  • Chemistry (all)

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