Geology

Molecular dynamics

Computer science

Optics

Bimetallic strip

Oceanography

Absorption spectroscopy

Machine learning

Radial distribution function

Composite material

Metal

Nanotechnology

Organic chemistry

Coordination number

Atom (system on chip)

Embedded system

Chemical physics

Particle (ecology)

Artificial neural network

Extended X-ray absorption fine structure

Computational chemistry

Nanoparticle

Chemistry

Metallurgy

Materials science

Nanometre

Physics

Nano Letters

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 part...

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning