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An Informatics Approach for Designing Conducting Polymers.
Sahu, Harikrishna; Li, Hongmo; Chen, Lihua; Rajan, Arunkumar Chitteth; Kim, Chiho; Stingelin, Natalie; Ramprasad, Rampi.
Afiliación
  • Sahu H; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Li H; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Chen L; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Rajan AC; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Kim C; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Stingelin N; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
  • Ramprasad R; Department of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
ACS Appl Mater Interfaces ; 13(45): 53314-53322, 2021 Nov 17.
Article en En | MEDLINE | ID: mdl-34038635
Doping conjugated polymers, which are potential candidates for the next generation of organic electronics, is an effective strategy for manipulating their electrical conductivity. However, selecting a suitable polymer-dopant combination is exceptionally challenging because of the vastness of the chemical, configurational, and morphological spaces one needs to search. In this work, high-performance surrogate models, trained on available experimentally measured data, are developed to predict the p-type electrical conductivity and are used to screen a large candidate hypothetical data set of more than 800 000 polymer-dopant combinations. Promising candidates are identified for synthesis and device fabrication. Additionally, new design guidelines are extracted that verify and extend knowledge on important molecular fragments that correlate to high conductivity. Conductivity prediction models are also deployed at www.polymergenome.org for broader open-access community use.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos