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

Volume: 19, Issue: 1, Pages: 520 - 529
Published: Dec 3, 2018
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...
Paper Details
Title
Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning
Published Date
Dec 3, 2018
Volume
19
Issue
1
Pages
520 - 529
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.