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Cited by 286

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

Geometry optimization made simple with translation and rotation coordinates

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Wang, Lee-Ping, Song, Chenchen·2016·The Journal of Chemical Physics

Abstract

The effective description of molecular geometry is important for theoretical studies of intermolecular interactions. Here we introduce a new translation-rotation-internal coordinate (TRIC) system which explicitly includes the collective translations and rotations of molecules, or parts of molecules such as monomers or ligands, as degrees of freedom. The translations are described as the centroid position and the orientations are represented with the exponential map parameterization of quaternions. When TRIC is incorporated into geometry optimization calculations, the performance is consistently superior to existing coordinate systems for a diverse set of systems including water clusters, organic semiconductor donor-acceptor complexes, and small proteins, all of which are characterized by nontrivial intermolecular interactions. The method also introduces a new way to scan the molecular orientations while allowing orthogonal degrees of freedom to relax. Our findings indicate that an explicit description of molecular translation and rotation is a natural way to traverse the many-dimensional potential energy surface.

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Metadata

Authors
Wang, L.-P., & Song, C.
Venue
The Journal of Chemical Physics
Published
2016-06-06
Volume
144
Issue
21
Pages
214108-214108
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W2412387777
Cited by
286
References
2
FWCI
2.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Geometry optimization made simple with translation and rotation coordinates. https://app.cochat.ai/papers/10.1063/1.4952956
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Wang, L.-P., & Song, C. (2016). Geometry optimization made simple with translation and rotation coordinates. The Journal of Chemical Physics. https://doi.org/10.1063/1.4952956

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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.