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Machine learning for molecular and materials science

Overview of attention for article published in Nature, July 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 (99th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

news
3 news outlets
blogs
4 blogs
twitter
358 tweeters
facebook
6 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
689 Mendeley
Title
Machine learning for molecular and materials science
Published in
Nature, July 2018
DOI 10.1038/s41586-018-0337-2
Pubmed ID
Authors

Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh

Abstract

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

Twitter Demographics

The data shown below were collected from the profiles of 358 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 689 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 689 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 199 29%
Researcher 156 23%
Student > Master 72 10%
Unspecified 65 9%
Student > Bachelor 61 9%
Other 136 20%
Readers by discipline Count As %
Chemistry 159 23%
Materials Science 119 17%
Unspecified 104 15%
Physics and Astronomy 87 13%
Engineering 64 9%
Other 156 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 261. 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 08 March 2019.
All research outputs
#42,126
of 12,680,115 outputs
Outputs from Nature
#4,677
of 65,621 outputs
Outputs of similar age
#2,080
of 275,459 outputs
Outputs of similar age from Nature
#215
of 912 outputs
Altmetric has tracked 12,680,115 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 65,621 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 73.7. This one has done particularly well, scoring higher than 92% 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 275,459 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 99% of its contemporaries.
We're also able to compare this research output to 912 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.