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Cited by 369

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research

Open access
Hafner, Jürgen, Wolverton, Christopher, Ceder, Gerbrand·2006·MRS Bulletin

Abstract

The development of modern materials science has led to a growing need to understand the phenomena determining the properties of materials and processes on an atomistic level. The interactions between atoms and electrons are governed by the laws of quantum mechanics; hence, accurate and efficient techniques for solving the basic quantum-mechanical equations for complex many-atom, many-electron systems must be developed. Density functional theory (DFT) marks a decisive breakthrough in these efforts, and in the past decade DFT has had a rapidly growing impact not only on fundamental but also industrial research. This article discusses the fundamental principles of DFT and the highly efficient computational tools that have been developed for its application to complex problems in materials science. Also highlighted are state-of-the-art applications in many areas of materials research, such as structural materials, catalysis and surface science, nanomaterials, and biomaterials and geophysics.

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Crossref · 10.1557/mrs2006.174

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Metadata

Authors
Hafner, J., Wolverton, C., & Ceder, G.
Venue
MRS Bulletin
Published
2006-09
Volume
31
Issue
9
Pages
659-668
ISSN
  • 0883-7694 (ISSN-L)
  • 1938-1425
OpenAlex
W2157955200
Cited by
369
References
59
FWCI
7.52
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research. https://app.cochat.ai/papers/10.1557/mrs2006.174
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Hafner, J., Wolverton, C., & Ceder, G. (2006). Toward Computational Materials Design: The Impact of Density Functional Theory on Materials Research. MRS Bulletin. https://doi.org/10.1557/mrs2006.174

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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.