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Information Science for Materials Discovery and Design

Overview of attention for book
Cover of 'Information Science for Materials Discovery and Design'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A Perspective on Materials Informatics: State-of-the-Art and Challenges
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    Chapter 2 Information-Driven Experimental Design in Materials Science
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    Chapter 3 Bayesian Optimization for Materials Design
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    Chapter 4 Small-Sample Classification
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    Chapter 5 Data Visualization and Structure Identification
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    Chapter 6 Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
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    Chapter 7 On the Use of Data Mining Techniques to Build High-Density, Additively-Manufactured Parts
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    Chapter 8 Optimal Dopant Selection for Water Splitting with Cerium Oxides: Mining and Screening First Principles Data
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    Chapter 9 Toward Materials Discovery with First-Principles Datasets and Learning Methods
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    Chapter 10 Materials Informatics Using Ab initio Data: Application to MAX Phases
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    Chapter 11 Symmetry-Adapted Distortion Modes as Descriptors for Materials Informatics
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    Chapter 12 Discovering Electronic Signatures for Phase Stability of Intermetallics via Machine Learning
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    Chapter 13 Combinatorial Materials Science, and a Perspective on Challenges in Data Acquisition, Analysis and Presentation
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    Chapter 14 High Throughput Combinatorial Experimentation + Informatics = Combinatorial Science
Attention for Chapter 6: Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

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99 Dimensions

Readers on

mendeley
17 Mendeley
Summary X Dimensions citations
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Chapter title
Inference of Hidden Structures in Complex Physical Systems by Multi-scale Clustering
Chapter number 6
Book title
Information Science for Materials Discovery and Design
Published in
arXiv, January 2016
DOI 10.1007/978-3-319-23871-5_6
Book ISBNs
978-3-31-923870-8, 978-3-31-923871-5
Authors

Z. Nussinov, P. Ronhovde, Dandan Hu, S. Chakrabarty, M. Sahu, Bo Sun, N. A. Mauro, K. K. Sahu, Nicholas A. Mauro, Kisor K. Sahu

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 35%
Student > Ph. D. Student 4 24%
Professor 2 12%
Student > Doctoral Student 2 12%
Student > Bachelor 1 6%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Physics and Astronomy 10 59%
Agricultural and Biological Sciences 1 6%
Computer Science 1 6%
Decision Sciences 1 6%
Neuroscience 1 6%
Other 2 12%
Unknown 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 March 2015.
All research outputs
#18,616,159
of 23,891,012 outputs
Outputs from arXiv
#477,000
of 994,254 outputs
Outputs of similar age
#273,975
of 398,844 outputs
Outputs of similar age from arXiv
#4,982
of 14,180 outputs
Altmetric has tracked 23,891,012 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 994,254 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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 398,844 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14,180 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.

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