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Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

Overview of attention for article published in Physical Review Letters, May 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
11 news outlets
blogs
1 blog
twitter
1 tweeter

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
101 Mendeley
citeulike
1 CiteULike
Title
Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy
Published in
Physical Review Letters, May 2018
DOI 10.1103/physrevlett.120.225502
Pubmed ID
Authors

Janis Timoshenko, Andris Anspoks, Arturs Cintins, Alexei Kuzmin, Juris Purans, Anatoly I. Frenkel

Abstract

The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 32%
Researcher 21 21%
Student > Postgraduate 6 6%
Student > Master 6 6%
Student > Bachelor 6 6%
Other 10 10%
Unknown 20 20%
Readers by discipline Count As %
Materials Science 25 25%
Physics and Astronomy 19 19%
Chemistry 13 13%
Chemical Engineering 9 9%
Engineering 4 4%
Other 7 7%
Unknown 24 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 82. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 September 2020.
All research outputs
#305,967
of 17,370,809 outputs
Outputs from Physical Review Letters
#930
of 31,241 outputs
Outputs of similar age
#9,619
of 288,309 outputs
Outputs of similar age from Physical Review Letters
#39
of 634 outputs
Altmetric has tracked 17,370,809 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 31,241 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 288,309 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 634 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.