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X Demographics
Mendeley readers
Attention Score in Context
| 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
The data shown below were collected from the profiles of 315 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 52 | 17% |
| United Kingdom | 40 | 13% |
| India | 10 | 3% |
| France | 10 | 3% |
| Spain | 8 | 3% |
| Canada | 7 | 2% |
| Germany | 6 | 2% |
| Japan | 5 | 2% |
| Switzerland | 5 | 2% |
| Other | 47 | 15% |
| Unknown | 125 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 171 | 54% |
| Scientists | 135 | 43% |
| Practitioners (doctors, other healthcare professionals) | 5 | 2% |
| Science communicators (journalists, bloggers, editors) | 4 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 3,503 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3503 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 795 | 23% |
| Researcher | 508 | 15% |
| Student > Master | 349 | 10% |
| Student > Bachelor | 249 | 7% |
| Student > Doctoral Student | 151 | 4% |
| Other | 477 | 14% |
| Unknown | 974 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 587 | 17% |
| Materials Science | 474 | 14% |
| Engineering | 336 | 10% |
| Physics and Astronomy | 249 | 7% |
| Computer Science | 183 | 5% |
| Other | 537 | 15% |
| Unknown | 1137 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 257. 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
#145,460
of 25,732,188 outputs
Outputs from Nature
#9,269
of 98,606 outputs
Outputs of similar age
#2,918
of 342,285 outputs
Outputs of similar age from Nature
#193
of 943 outputs
Altmetric has tracked 25,732,188 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 98,606 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 102.6. 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 342,285 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.