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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 (89th percentile)

Mentioned by

news
11 news outlets
blogs
1 blog

Readers on

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

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.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 30%
Researcher 4 20%
Unspecified 3 15%
Student > Postgraduate 2 10%
Student > Bachelor 1 5%
Other 1 5%
Unknown 3 15%
Readers by discipline Count As %
Physics and Astronomy 6 30%
Unspecified 4 20%
Materials Science 4 20%
Chemistry 2 10%
Engineering 1 5%
Other 0 0%
Unknown 3 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 80. 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 07 June 2018.
All research outputs
#161,055
of 11,707,803 outputs
Outputs from Physical Review Letters
#569
of 21,427 outputs
Outputs of similar age
#8,290
of 253,235 outputs
Outputs of similar age from Physical Review Letters
#39
of 370 outputs
Altmetric has tracked 11,707,803 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 21,427 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. 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 253,235 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 370 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.