Properties
of mono- and bimetallic metal nanoparticles (NPs) may
depend strongly on their compositional, structural (or geometrical)
attributes, and their atomic dynamics, all of which can be efficiently
described by a partial radial distribution function (PRDF) of metal
atoms. For NPs that are several nanometers in size, finite size effects
may play a role in determining crystalline order, interatomic distances,
and particle shape. Bimetallic NPs may also have different compositional
distributions than bulk materials. These factors all render the determination
of PRDFs challenging. Here extended X-ray absorption fine structure
(EXAFS) spectroscopy, molecular dynamics simulations, and supervised
machine learning (artificial neural-network) method are combined to
extract PRDFs directly from experimental data. By applying this method
to several systems of Pt and PdAu NPs, we demonstrate the finite size
effects on the nearest neighbor distributions, bond dynamics, and
alloying motifs in mono- and bimetallic particles and establish the
generality of this approach.