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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 (79th percentile)

Mentioned by

news
5 news outlets
blogs
7 blogs
policy
1 policy source
twitter
313 X users
patent
8 patents
facebook
6 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
2691 Dimensions

Readers on

mendeley
3589 Mendeley
citeulike
1 CiteULike
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 313 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,589 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3589 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 802 22%
Researcher 512 14%
Student > Master 359 10%
Student > Bachelor 254 7%
Student > Doctoral Student 151 4%
Other 486 14%
Unknown 1025 29%
Readers by discipline Count As %
Chemistry 595 17%
Materials Science 476 13%
Engineering 343 10%
Physics and Astronomy 251 7%
Computer Science 183 5%
Other 553 15%
Unknown 1188 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 260. 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 12 March 2024.
All research outputs
#148,386
of 26,352,912 outputs
Outputs from Nature
#9,366
of 100,298 outputs
Outputs of similar age
#2,920
of 345,327 outputs
Outputs of similar age from Nature
#192
of 943 outputs
Altmetric has tracked 26,352,912 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 100,298 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 103.1. This one has done particularly well, scoring higher than 90% 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 345,327 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 943 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.