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Cited by 1,157

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AtomEye: an efficient atomistic configuration viewer

Open access
Li, Ju·2003·Modelling and Simulation in Materials Science and Engineering

Abstract

AtomEye is free atomistic visualization software for all major UNIX platforms. It is based on a newly developed graphics core library of higher quality than the X-window standard, with area-weighted anti-aliasing. An order- N neighbourlist algorithm is used to compute the bond connectivity. The functionalities of AtomEye include: parallel and perspective projections with full three-dimensional navigation; customizable bond and coordination number calculation; colour-encoding of arbitrary user-defined quantities; local atomic strain invariant; coloured atom tiling and tracing; up to 16 cutting planes; periodic boundary condition translations; high-quality JPEG, PNG and EPS screenshots; and animation scripting. The program is efficient compared to OpenGL hardware acceleration by employing special algorithms to treat spheres (atoms) and cylinders (bonds), in which they are rendered as primitive objects rather than as composites of polygons. AtomEye can handle more than one million atoms on a PC with 1 GB memory. It is a robust, low-cost tool for surveying nanostructures and following their evolutions.

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Metadata

Authors
Li, J.
Venue
Modelling and Simulation in Materials Science and Engineering
Published
2003-01-30
Volume
11
Issue
2
Pages
173-177
ISSN
0965-0393 (ISSN-L)
OpenAlex
W2135827618
Cited by
1,157
References
2
FWCI
29.39
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: AtomEye: an efficient atomistic configuration viewer. https://app.cochat.ai/papers/10.1088/0965-0393/11/2/305
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Li, J. (2003). AtomEye: an efficient atomistic configuration viewer. Modelling and Simulation in Materials Science and Engineering. https://doi.org/10.1088/0965-0393/11/2/305

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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.