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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
twitter
314 X users
patent
6 patents
facebook
6 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
2454 Dimensions

Readers on

mendeley
3477 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 314 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,477 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3477 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 793 23%
Researcher 503 14%
Student > Master 347 10%
Student > Bachelor 253 7%
Student > Doctoral Student 150 4%
Other 501 14%
Unknown 930 27%
Readers by discipline Count As %
Chemistry 589 17%
Materials Science 473 14%
Engineering 331 10%
Physics and Astronomy 248 7%
Computer Science 181 5%
Other 565 16%
Unknown 1090 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 258. 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 24 January 2024.
All research outputs
#142,075
of 25,371,288 outputs
Outputs from Nature
#9,145
of 97,773 outputs
Outputs of similar age
#2,889
of 341,211 outputs
Outputs of similar age from Nature
#194
of 943 outputs
Altmetric has tracked 25,371,288 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 97,773 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 102.4. 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 341,211 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.