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Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network

Overview of attention for article published in Journal of Chemical Information and Modeling, February 2020
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41 Mendeley
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Article details
Title
Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network
Published in
Journal of Chemical Information and Modeling, February 2020
DOI 10.1021/acs.jcim.9b00835
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 11 27%
Student > Ph. D. Student 7 17%
Student > Master 6 15%
Student > Doctoral Student 3 7%
Student > Bachelor 2 5%
Other 1 2%
Unknown 11 27%
Readers by discipline
Readers by discipline Count As %
Chemistry 14 34%
Materials Science 4 10%
Engineering 4 10%
Physics and Astronomy 2 5%
Social Sciences 1 2%
Other 1 2%
Unknown 15 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 April 2020.
All research outputs
#22,456,121
of 33,834,589 outputs
Outputs from Journal of Chemical Information and Modeling
#5,731
of 8,186 outputs
Outputs of similar age
#319,008
of 524,225 outputs
Outputs of similar age from Journal of Chemical Information and Modeling
#102
of 155 outputs
Altmetric has tracked 33,834,589 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,186 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 524,225 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 155 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.