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Ab initio molecular dynamics using hybrid density functionals

Open access
Guidon, Manuel, Schiffmann, Florian, Hutter, Jürg, VandeVondele, Joost·2008·The Journal of Chemical Physics

Abstract

Ab initio molecular dynamics simulations with hybrid density functionals have so far found little application due to their computational cost. In this work, an implementation of the Hartree–Fock exchange is presented that is specifically targeted at ab initio molecular dynamics simulations of medium sized systems. We demonstrate that our implementation, which is available as part of the CP2K/Quickstep program, is robust and efficient. Several prescreening techniques lead to a linear scaling cost for integral evaluation and storage. Integral compression techniques allow for in-core calculations on systems containing several thousand basis functions. The massively parallel implementation respects integral symmetry and scales up to hundreds of CPUs using a dynamic load balancing scheme. A time-reversible multiple time step scheme, exploiting the difference in computational efficiency between hybrid and local functionals, brings further time savings. With extensive simulations of liquid water, we demonstrate the ability to perform, for several tens of picoseconds, ab initio molecular dynamics based on hybrid functionals of systems in the condensed phase containing a few thousand Gaussian basis functions.

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Metadata

Authors
Guidon, M., Schiffmann, F., Hutter, J., & VandeVondele, J.
Venue
The Journal of Chemical Physics
Published
2008-06-04
Volume
128
Issue
21
Pages
214104-214104
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W2017535115
Cited by
253
References
0
FWCI
9.28
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Ab initio molecular dynamics using hybrid density functionals. https://app.cochat.ai/papers/10.1063/1.2931945
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Guidon, M., Schiffmann, F., Hutter, J., & VandeVondele, J. (2008). Ab initio molecular dynamics using hybrid density functionals. The Journal of Chemical Physics. https://doi.org/10.1063/1.2931945

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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.