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We present a novel approach for finding and evaluating structural models ofsmall metallic nanoparticles. Rather than fitting a single model with manydegrees of freedom, the approach algorithmically builds libraries ofnanoparticle clusters from multiple structural motifs, and individually fitsthem to experimental PDFs. Each cluster-fit is highly constrained. Theapproach, called cluster-mining, returns all candidate structure models thatare consistent with the data as measured by a goodness of fit. It is highlyautomated, easy to use, and yields models that are more physically realisticand result in better agreement to the data than models based on cubicclose-packed crystallographic cores, often reported in the literature formetallic nanoparticles.

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data