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Cited by 236

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Advances in Correlated Electronic Structure Methods for Solids, Surfaces, and Nanostructures

Huang, Patrick, Carter, Emily A.·2008·Annual Review of Physical Chemistry

Abstract

Calculations of the electronic structure of solids began decades ago, but only recently have solid-state quantum techniques become sufficiently reliable that their application is nearly as routine as quantum chemistry is for molecules. We aim to introduce chemists to the pros and cons of first-principles methods that can provide atomic-scale insight into the properties and chemistry of bulk materials, interfaces, and nanostructures. The techniques we review include the ubiquitous density functional theory (DFT), which is often sufficient, especially for metals; extensions such as DFT + U and hybrid DFT, which incorporate exact exchange to rid DFT of its spurious self-interactions (critical for some semiconductors and strongly correlated materials); many-body Green's function (GW and Bethe-Salpeter) methods for excited states; quantum Monte Carlo, in principle an exact theory but for which forces (hence structure optimization and dynamics) are problematic; and embedding theories that locally refine the quantum treatment to improve accuracy.

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Crossref · 10.1146/annurev.physchem.59.032607.093528

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Metadata

Authors
Huang, P., & Carter, E. A.
Venue
Annual Review of Physical Chemistry
Published
2008-05-01
Volume
59
Issue
1
Pages
261-290
ISSN
  • 0066-426X (ISSN-L)
  • 1545-1593
OpenAlex
W2117103384
Cited by
236
References
2
FWCI
0.97
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Advances in Correlated Electronic Structure Methods for Solids, Surfaces, and Nanostructures. https://app.cochat.ai/papers/10.1146/annurev.physchem.59.032607.093528
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Huang, P., & Carter, E. A. (2008). Advances in Correlated Electronic Structure Methods for Solids, Surfaces, and Nanostructures. Annual Review of Physical Chemistry. https://doi.org/10.1146/annurev.physchem.59.032607.093528

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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.