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Cited by 167

The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.

Toward Design of Novel Materials for Organic Electronics

Open access
Friederich, Pascal, Fediai, Artem, Kaiser, Simon, Konrad, Manuel, Jung, Nicole, Wenzel, Wolfgang·2019·Advanced Materials

Abstract

Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large‐area devices, and thin‐film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials.

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Crossref · 10.1002/adma.201808256

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Metadata

Authors
Friederich, P., Fediai, A., Kaiser, S., Konrad, M., Jung, N., & Wenzel, W.
Venue
Advanced Materials
Publisher
Wiley
Published
2019-04-22
Volume
31
Issue
26
Pages
e1808256-e1808256
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W2941507793
Cited by
167
References
20
FWCI
7.12
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Toward Design of Novel Materials for Organic Electronics. https://app.cochat.ai/papers/10.1002/adma.201808256
The paper
Friederich, P., Fediai, A., Kaiser, S., Konrad, M., Jung, N., & Wenzel, W. (2019). Toward Design of Novel Materials for Organic Electronics. Advanced Materials. https://doi.org/10.1002/adma.201808256

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.