↓ Skip to main content

Machine learning for molecular and materials science

Overview of attention for article published in Nature, July 2018
Altmetric Badge

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
352 tweeters
facebook
6 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
563 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 352 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 563 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 563 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 161 29%
Researcher 133 24%
Student > Master 55 10%
Student > Bachelor 53 9%
Unspecified 48 9%
Other 113 20%
Readers by discipline Count As %
Chemistry 137 24%
Materials Science 92 16%
Unspecified 77 14%
Physics and Astronomy 72 13%
Engineering 52 9%
Other 133 24%

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 16 January 2019.
All research outputs
#40,534
of 12,371,375 outputs
Outputs from Nature
#4,544
of 64,908 outputs
Outputs of similar age
#2,049
of 269,453 outputs
Outputs of similar age from Nature
#215
of 912 outputs
Altmetric has tracked 12,371,375 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 64,908 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 72.3. 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 269,453 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.