{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Makki H"],"funding":["European Research Council"],"pagination":["5723-5732"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12089976"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(15)"],"pubmed_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, the predictive power of machine learning models trained on our dataset highlights the potential of data-driven approaches to SCP design, laying the groundwork for accelerated discovery of materials with tailored electronic properties."],"journal":["Materials horizons"],"pubmed_title":["Mapping the structure-function landscape of semiconducting polymers."],"pmcid":["PMC12089976"],"funding_grant_id":["101020369"],"pubmed_authors":["Makki H","Burke C","Nielsen CB","Troisi A"],"additional_accession":[]},"is_claimable":false,"name":"Mapping the structure-function landscape of semiconducting polymers.","description":"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, the predictive power of machine learning models trained on our dataset highlights the potential of data-driven approaches to SCP design, laying the groundwork for accelerated discovery of materials with tailored electronic properties.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-03-18T13:52:49.683Z","creation":"2025-08-21T09:51:40.778Z"},"accession":"S-EPMC12089976","cross_references":{"pubmed":["40390597"],"doi":["10.1039/d5mh00485c"]}}