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Cited by 411

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Charge Transport in Molecular Materials: An Assessment of Computational Methods

Open access
Oberhofer, Harald, Reuter, Karsten, Blumberger, Jochen·2017·Chemical Reviews

Abstract

The booming field of molecular electronics has fostered a surge of computational research on electronic properties of organic molecular solids. In particular, with respect to a microscopic understanding of transport and loss mechanisms, theoretical studies assume an ever-increasing role. Owing to the tremendous diversity of organic molecular materials, a great number of computational methods have been put forward to suit every possible charge transport regime, material, and need for accuracy. With this review article we aim at providing a compendium of the available methods, their theoretical foundations, and their ranges of validity. We illustrate these through applications found in the literature. The focus is on methods available for organic molecular crystals, but mention is made wherever techniques are suitable for use in other related materials such as disordered or polymeric systems.

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Crossref · 10.1021/acs.chemrev.7b00086

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Metadata

Authors
Oberhofer, H., Reuter, K., & Blumberger, J.
Venue
Chemical Reviews
Published
2017-06-23
Volume
117
Issue
15
Pages
10319-10357
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
OpenAlex
W2685750502
Cited by
411
References
6
FWCI
2.41
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Charge Transport in Molecular Materials: An Assessment of Computational Methods. https://app.cochat.ai/papers/10.1021/acs.chemrev.7b00086
The paper
Oberhofer, H., Reuter, K., & Blumberger, J. (2017). Charge Transport in Molecular Materials: An Assessment of Computational Methods. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.7b00086

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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.