scholarly journals Cluster-mining: an approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

2020 ◽  
Vol 76 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Soham Banerjee ◽  
Chia-Hao Liu ◽  
Kirsten M. Ø. Jensen ◽  
Pavol Juhás ◽  
Jennifer D. Lee ◽  
...  
Keyword(s):  
Metallic Nanoparticles ◽  
Degrees Of Freedom ◽  
Goodness Of Fit ◽  
Distribution Functions ◽  
Single Model ◽  
Atomic Pair Distribution Function ◽  
Novel Approach ◽  
Atomic Pair ◽  
Experimental Pair ◽  
Pair Distribution Functions

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.

Structure of crystallographically challenged materials by profile analysis of atomic pair distribution functions: study of LiMoS2 and mesostructured MnGe4S10.

2001 ◽  
Vol 678 ◽  
Author(s):  
V. Petkov ◽  
K. K. Rangan ◽  
M. G. Kanatzidis ◽  
S.J.L. Billinge
Keyword(s):  
Distribution Function ◽  
Pair Distribution Function ◽  
Profile Analysis ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Unit Cell ◽  
Atomic Pair Distribution Function ◽  
Atomic Pair ◽  
Pair Distribution Functions ◽  
Triclinic Unit Cell

AbstractThe approach of the atomic pair distribution function (PDF) technique to study the structure of materials with significant disorder is considered and successfully applied to LiMoS2 and mesostructured MnGe4S10. We find that LiMoS2 is built of layers of distorted MoS6 octahedra stacked along the c axis of a triclinic unit cell with well-defined Mo-Mo bonding. Mesostructured MnGe4S10 is a three-dimensional framework of “adamantane-like” [Ge4S10] units bridged by Mn atoms.

Atomic pair distribution functions analysis of disordered low-Z materials

2013 ◽  
Vol 15 (22) ◽  
pp. 8544 ◽  
Author(s):  
V. Petkov ◽  
Y. Ren ◽  
S. Kabekkodu ◽  
D. Murphy
Keyword(s):  
Distribution Functions ◽  
Atomic Pair ◽  
Pair Distribution Functions

Algorithm for systematic peak extraction from atomic pair distribution functions

2015 ◽  
Vol 71 (4) ◽  
pp. 392-409 ◽  
Author(s):  
L. Granlund ◽  
S. J. L. Billinge ◽  
P. M. Duxbury
Keyword(s):  
Structural Model ◽  
Information Criterion ◽  
Distribution Functions ◽  
Theory And Practice ◽  
Chi Square ◽  
Information Theoretic ◽  
Atomic Pair ◽  
Structure Solution ◽  
The Impact ◽  
Pair Distribution Functions

The study presents an algorithm, ParSCAPE, for model-independent extraction of peak positions and intensities from atomic pair distribution functions (PDFs). It provides a statistically motivated method for determining parsimony of extracted peak models using the information-theoretic Akaike information criterion (AIC) applied to plausible models generated within an iterative framework of clustering and chi-square fitting. All parameters the algorithm uses are in principle known or estimable from experiment, though careful judgment must be applied when estimating the PDF baseline of nanostructured materials. ParSCAPE has been implemented in the Python programSrMise. Algorithm performance is examined on synchrotron X-ray PDFs of 16 bulk crystals and two nanoparticles using AIC-based multimodeling techniques, and particularly the impact of experimental uncertainties on extracted models. It is quite resistant to misidentification of spurious peaks coming from noise and termination effects, even in the absence of a constraining structural model. Structure solution from automatically extracted peaks using the Liga algorithm is demonstrated for 14 crystals and for C60. Special attention is given to the information content of the PDF, theory and practice of the AIC, as well as the algorithm's limitations.

Modelling pair distribution functions (PDFs) of organic compounds: describing both intra- and intermolecular correlation functions in calculated PDFs

2015 ◽  
Vol 48 (1) ◽  
pp. 171-178 ◽  
Author(s):  
Dragica Prill ◽  
Pavol Juhás ◽  
Martin U. Schmidt ◽  
Simon J. L. Billinge
Keyword(s):  
Structural Models ◽  
Simple Approach ◽  
Distribution Functions ◽  
Atomic Motion ◽  
Molecular Systems ◽  
Atomic Pair ◽  
Intermolecular Distances ◽  
Supercell Model ◽  
Pair Distribution Functions ◽  
Modelling Approach

The methods currently used to calculate atomic pair distribution functions (PDFs) from organic structural models do not distinguish between the intramolecular and intermolecular distances. Owing to the stiff bonding between atoms within a molecule, the PDF peaks arising from intramolecular atom–atom distances are much sharper than those of the intermolecular atom–atom distances. This work introduces a simple approach to calculate PDFs of molecular systems without building a supercell model by using two different isotropic displacement parameters to describe atomic motion: one parameter is used for the intramolecular, the other one for intermolecular atom–atom distances. Naphthalene, quinacridone and paracetamol were used as examples. Calculations were done with theDiffPy-CMIcomplex modelling infrastructure. The new modelling approach produced remarkably better fits to the experimental PDFs, confirming the higher accuracy of this method for organic materials.

scholarly journals Analysis of stacking disorder in ice I using pair distribution functions

2018 ◽  
Vol 51 (4) ◽  
pp. 1211-1220 ◽  
Author(s):  
Helen Y. Playford ◽  
Thomas F. Whale ◽  
Benjamin J. Murray ◽  
Matt G. Tucker ◽  
Christoph G. Salzmann
Keyword(s):  
Chemical Properties ◽  
Two Dimensions ◽  
Distribution Functions ◽  
Memory Effects ◽  
Framework Materials ◽  
Experimental Pair ◽  
Physical And Chemical ◽  
Pair Distribution Functions ◽  
Stacking Disorder ◽  
And Chemical Properties

Stacking-disordered materials display crystalline order in two dimensions but are disordered along the direction in which layered structural motifs are stacked. Countless examples of stacking disorder exist, ranging from close-packed metals, ice I and diamond to open-framework materials and small-molecule pharmaceuticals. In general, the presence of stacking disorder can have profound consequences for the physical and chemical properties of a material. Traditional analyses of powder diffraction data are often complicated by the presence of memory effects in the stacking sequences. Here it is shown that experimental pair distribution functions of stacking-disordered ice I can be used to determine local information on the fractions of cubic and hexagonal stacking. Ice is a particularly challenging material in this respect, since both the stacking disorder and the orientational disorder of the water molecules need to be described. Memory effects are found to contribute very little to the pair distribution functions, and consequently, the analysis of pair distribution functions is the method of choice for characterizing stacking-disordered samples with complicated and high-order memory effects. In the context of this work, the limitations of current structure-reconstruction approaches are also discussed.

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