Stony Brook Univ., NY (United States). Dept. of Materials Science and Chemical Engineering
Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis, Dept. of Chemical Engineering
Harvard Univ., Cambridge, MA (United States). Dept. of Chemistry and Chemical Biology
Harvard Univ., Cambridge, MA (United States). John A. Paulson School of Engineering and Applied Sciences, and Wyss Inst. for Biologically Inspired Engineering
SLAC National Accelerator Lab., Menlo Park, CA (United States). Stanford Synchrotron Radiation Lightsource (SSRL)
Harvard Univ., Cambridge, MA (United States). Dept. of Chemistry and Chemical Biology, John A. Paulson School of Engineering and Applied Sciences, and Wyss Inst. for Biologically Inspired Engineering
Harvard Univ., Cambridge, MA (United States). Dept. of Chemistry and Chemical Biology, and John A. Paulson School of Engineering and Applied Sciences
Stony Brook Univ., NY (United States). Dept. of Materials Science and Chemical Engineering; Brookhaven National Lab. (BNL), Upton, NY (United States). Division of Chemistry
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. Finally, 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.
@article{osti_1493187,
author = {Timoshenko, Janis and Wrasman, Cody J. and Luneau, Mathilde and Shirman, Tanya and Cargnello, Matteo and Bare, Simon R. and Aizenberg, Joanna and Friend, Cynthia M. and Frenkel, Anatoly I.},
title = {Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning},
annote = {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. Finally, 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.},
doi = {10.1021/acs.nanolett.8b04461},
url = {https://www.osti.gov/biblio/1493187},
journal = {Nano Letters},
issn = {ISSN 1530-6984},
number = {1},
volume = {19},
place = {United States},
publisher = {American Chemical Society},
year = {2018},
month = {12}}
Energy Frontier Research Centers (EFRC) (United States). Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC); Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)