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Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

Overview of attention for article published in npj Computational Materials, June 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 532)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
6 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
84 Mendeley
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Title
Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials
Published in
npj Computational Materials, June 2020
DOI 10.1038/s41524-020-00352-0
Authors

Yabo Dan, Yong Zhao, Xiang Li, Shaobo Li, Ming Hu, Jianjun Hu

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 26%
Student > Master 15 18%
Researcher 12 14%
Student > Bachelor 7 8%
Other 4 5%
Other 7 8%
Unknown 17 20%
Readers by discipline Count As %
Materials Science 25 30%
Chemistry 12 14%
Engineering 9 11%
Physics and Astronomy 8 10%
Computer Science 7 8%
Other 6 7%
Unknown 17 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 64. 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 01 November 2020.
All research outputs
#408,972
of 17,595,144 outputs
Outputs from npj Computational Materials
#16
of 532 outputs
Outputs of similar age
#13,939
of 294,129 outputs
Outputs of similar age from npj Computational Materials
#1
of 1 outputs
Altmetric has tracked 17,595,144 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 532 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. 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 294,129 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them