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Cluster‐mining : an approach for determining core structures of metallic nanoparticles from atomic pair distribution function data
Author(s) -
Banerjee Soham,
Liu Chia-Hao,
Jensen Kirsten M. Ø.,
Juhás Pavol,
Lee Jennifer D.,
Tofanelli Marcus,
Ackerson Christopher J.,
Murray Christopher B.,
Billinge Simon J. L.
Publication year - 2020
Publication title -
acta crystallographica section a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.742
H-Index - 83
ISSN - 2053-2733
DOI - 10.1107/s2053273319013214
Subject(s) - cluster (spacecraft) , nanoparticle , goodness of fit , core (optical fiber) , pair distribution function , distribution function , function (biology) , metal , materials science , key (lock) , distribution (mathematics) , degrees of freedom (physics and chemistry) , computer science , data mining , statistical physics , biological system , computational science , nanotechnology , physics , mathematics , thermodynamics , mathematical analysis , machine learning , evolutionary biology , biology , quantum mechanics , computer security , metallurgy , composite material , programming language
A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster‐mining , returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close‐packed crystallographic cores, often reported in the literature for metallic nanoparticles.