Luneau Lab

at Chalmers University of Technology in Gothenburg, Sweden

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


Journal article


J. Timoshenko, Cody J. Wrasman, M. Luneau, T. Shirman, M. Cargnello, S. Bare, J. Aizenberg, C. Friend, A. Frenkel
Nano letters (Print), 2018

Semantic Scholar DOI PubMed
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APA   Click to copy
Timoshenko, J., Wrasman, C. J., Luneau, M., Shirman, T., Cargnello, M., Bare, S., … Frenkel, A. (2018). Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning. Nano Letters (Print).


Chicago/Turabian   Click to copy
Timoshenko, J., Cody J. Wrasman, M. Luneau, T. Shirman, M. Cargnello, S. Bare, J. Aizenberg, C. Friend, and A. Frenkel. “Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning.” Nano letters (Print) (2018).


MLA   Click to copy
Timoshenko, J., et al. “Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning.” Nano Letters (Print), 2018.


BibTeX   Click to copy

@article{j2018a,
  title = {Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning.},
  year = {2018},
  journal = {Nano letters (Print)},
  author = {Timoshenko, J. and Wrasman, Cody J. and Luneau, M. and Shirman, T. and Cargnello, M. and Bare, S. and Aizenberg, J. and Friend, C. and Frenkel, A.}
}

Abstract

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.


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