Mapping the Structure-Function Landscape of Semiconducting Polymers

01 April 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The molecular design of semiconducting polymers (SCPs) has been largely guided by varying monomer combinations and sequences by leveraging a robust understanding of charge transport mechanisms. However, the connection between controllable structural features and resulting electronic disorder remains elusive, leaving design rules for next-generation SCPs undefined. Using high-throughput computational methods, we analyse 100+ state-of-the-art p- and n-type polymer models. This exhaustive dataset allows for deriving statistically significant design rules. Our analysis disentangles the impact of key structural features, examining existing hypotheses, and identifying new structure-property relationships. For instance, we show that polymer rigidity has minimal impact on charge transport, while the planarity persistence length, introduced here, is a superior structural characteristic. Additionally, we demonstrate the predictive power of machine learning models built on our dataset, laying the groundwork for a data-driven approach to SCP design and accelerating the discovery of materials with tailored electronic properties. 

Keywords

Semiconducting Polymers
Virtual Screening
Charge transport

Supplementary materials

Title
Description
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Title
SI for Mapping the Structure-Function Landscape of Semiconducting Polymers
Description
This document outlines the methodology and validation procedures for high-throughput calculations.
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