Cluster-mining: an approach for determining core structures of metallic nanoparticles from atomic pair distribution function data
Abstract
A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2020
- Source ID
- 10.1107/s2053273319013214
Entities
People
- Chia-hao Liu
- Christopher B Murray
- Christopher J. Ackerson
- Jennifer D. Lee
- Kirsten M. Ø. Jensen
- Marcus Tofanelli
- Pavol Juhás
- Simon Billinge
- Soham Banerjee
Organizations
- National Science Foundation
- Office of Science
- United States Department of Energy
- Villum Foundation