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RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells

Overview of attention for article published in Journal of Cheminformatics, June 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

news
1 news outlet
twitter
7 X users
patent
2 patents
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
42 Mendeley
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Title
RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells
Published in
Journal of Cheminformatics, June 2017
DOI 10.1186/s13321-017-0224-0
Pubmed ID
Authors

Omer Kaspi, Abraham Yosipof, Hanoch Senderowitz

Abstract

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 14%
Professor > Associate Professor 4 10%
Student > Bachelor 4 10%
Other 3 7%
Researcher 3 7%
Other 9 21%
Unknown 13 31%
Readers by discipline Count As %
Engineering 6 14%
Chemistry 5 12%
Computer Science 4 10%
Materials Science 3 7%
Mathematics 2 5%
Other 7 17%
Unknown 15 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 18 July 2023.
All research outputs
#1,707,001
of 25,492,047 outputs
Outputs from Journal of Cheminformatics
#117
of 970 outputs
Outputs of similar age
#32,738
of 331,904 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 17 outputs
Altmetric has tracked 25,492,047 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 970 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 10.0. This one has done well, scoring higher than 88% 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 331,904 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 90% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.