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Machine learning for the prediction of molecular dipole moments obtained by density functional theory

Overview of attention for article published in Journal of Cheminformatics, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Average Attention Score compared to outputs of the same age and source

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11 X users
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1 Facebook page

Citations

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33 Dimensions

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85 Mendeley
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Title
Machine learning for the prediction of molecular dipole moments obtained by density functional theory
Published in
Journal of Cheminformatics, August 2018
DOI 10.1186/s13321-018-0296-5
Pubmed ID
Authors

Florbela Pereira, João Aires-de-Sousa

Abstract

Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened. Random forest regression models predicted an external test set of 3368 compounds achieving mean absolute error up to 0.44 D. The results represent a significant improvement of the dipole moments calculated using empirical point charges located at the nucleus, even assuming the DFT-optimized geometry (root mean square error, RMSE, of 0.68 D vs. 1.53 D and R2 = 0.87 vs. 0.66).

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X Demographics

The data shown below were collected from the profiles of 11 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 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 85 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 26%
Researcher 15 18%
Student > Bachelor 9 11%
Student > Doctoral Student 6 7%
Student > Master 4 5%
Other 7 8%
Unknown 22 26%
Readers by discipline Count As %
Chemistry 32 38%
Physics and Astronomy 6 7%
Materials Science 6 7%
Computer Science 3 4%
Engineering 3 4%
Other 11 13%
Unknown 24 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 30 August 2018.
All research outputs
#6,132,277
of 25,079,131 outputs
Outputs from Journal of Cheminformatics
#464
of 942 outputs
Outputs of similar age
#97,709
of 339,393 outputs
Outputs of similar age from Journal of Cheminformatics
#13
of 18 outputs
Altmetric has tracked 25,079,131 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 942 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 50% 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 339,393 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.