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
