| 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. |
Mendeley readers
The data shown below were compiled from readership statistics for 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 132 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 42 | 32% |
| Researcher | 25 | 19% |
| Student > Bachelor | 7 | 5% |
| Student > Postgraduate | 6 | 5% |
| Student > Master | 6 | 5% |
| Other | 14 | 11% |
| Unknown | 32 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Materials Science | 27 | 20% |
| Physics and Astronomy | 22 | 17% |
| Chemistry | 18 | 14% |
| Chemical Engineering | 11 | 8% |
| Engineering | 6 | 5% |
| Other | 11 | 8% |
| Unknown | 37 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 78. 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
#472,754
of 23,083,773 outputs
Outputs from Physical Review Letters
#1,299
of 35,893 outputs
Outputs of similar age
#11,829
of 331,171 outputs
Outputs of similar age from Physical Review Letters
#49
of 622 outputs
Altmetric has tracked 23,083,773 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 35,893 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has done particularly well, scoring higher than 96% 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 331,171 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 622 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 92% of its contemporaries.