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Cited by 320

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Mean-field molecular dynamics with surface hopping

Prezhdo, Oleg V., Rossky, Peter J.·1997·The Journal of Chemical Physics

Abstract

Molecular dynamics simulations of many degree of freedom systems are often comprised of classical evolutions on quantum adiabatic energy surfaces with intermittent instantaneous hops from one surface to another. However, since quantum transitions are inherently nonadiabatic processes, the adiabatic approximation underlying the classical equations of motion does not hold in the regions where quantum transitions take place, and the restriction to classical trajectories for adiabatic quantum states is an approximation. Alternatives which employ classical paths that account more fully for nonadiabaticity can be computationally expensive and algorithmically complicated. Here, we propose a new method, which combines the surface hopping idea with the mean-field approximation for classical paths. Applied to three test systems, the method is shown to outperform the methods based on an adiabatic force without significant extra effort. This makes it an appealing alternative for modeling complex quantum–classical processes.

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Metadata

Authors
Prezhdo, O. V., & Rossky, P. J.
Venue
The Journal of Chemical Physics
Published
1997-07-15
Volume
107
Issue
3
Pages
825-834
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W2031305412
Cited by
320
References
24
FWCI
7.27
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mean-field molecular dynamics with surface hopping. https://app.cochat.ai/papers/10.1063/1.474382
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Prezhdo, O. V., & Rossky, P. J. (1997). Mean-field molecular dynamics with surface hopping. The Journal of Chemical Physics. https://doi.org/10.1063/1.474382

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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.