Two worlds collide: image analysis methods for quantifying structural variation in cluster molecular dynamics

Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of...

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Bibliographic Details
Published in:The Journal of chemical physics 2014-02, Vol.140 (6), p.064102-064102
Main Authors: Steenbergen, K G, Gaston, N
Format: Article
Language:English
Subjects:
Clusters
Correlation analysis
Correlation coefficients
Dynamic structural analysis
ELECTRONIC STRUCTURE
Image analysis
IMAGE PROCESSING
INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY
ISOMERS
Molecular dynamics
MOLECULAR DYNAMICS METHOD
Principal components analysis
REMOTE SENSING
SIMULATION
Stability analysis
Structural analysis
Structural stability
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Summary:Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD.
ISSN:0021-9606
1089-7690
DOI:10.1063/1.4864753