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Machine Learning Meets Quantum Physics

Overview of attention for book
Cover of 'Machine Learning Meets Quantum Physics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Introduction
  3. Altmetric Badge
    Chapter 2 Introduction to Material Modeling
  4. Altmetric Badge
    Chapter 3 Kernel Methods for Quantum Chemistry
  5. Altmetric Badge
    Chapter 4 Introduction to Neural Networks
  6. Altmetric Badge
    Chapter 5 Building Nonparametric n -Body Force Fields Using Gaussian Process Regression
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    Chapter 6 Machine-Learning of Atomic-Scale Properties Based on Physical Principles
  8. Altmetric Badge
    Chapter 7 Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches
  9. Altmetric Badge
    Chapter 8 Quantum Machine Learning with Response Operators in Chemical Compound Space
  10. Altmetric Badge
    Chapter 9 Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes
  11. Altmetric Badge
    Chapter 10 Message Passing Neural Networks
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    Chapter 11 Learning Representations of Molecules and Materials with Atomistic Neural Networks
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    Chapter 12 Molecular Dynamics with Neural Network Potentials
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    Chapter 13 High-Dimensional Neural Network Potentials for Atomistic Simulations
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    Chapter 14 Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
  16. Altmetric Badge
    Chapter 15 Active Learning and Uncertainty Estimation
  17. Altmetric Badge
    Chapter 16 Machine Learning for Molecular Dynamics on Long Timescales
  18. Altmetric Badge
    Chapter 17 Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design
  19. Altmetric Badge
    Chapter 18 Polymer Genome: A Polymer Informatics Platform to Accelerate Polymer Discovery
  20. Altmetric Badge
    Chapter 19 Bayesian Optimization in Materials Science
  21. Altmetric Badge
    Chapter 20 Recommender Systems for Materials Discovery
  22. Altmetric Badge
    Chapter 21 Generative Models for Automatic Chemical Design
Attention for Chapter 14: Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
9 X users

Citations

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69 Dimensions

Readers on

mendeley
48 Mendeley
Summary X Dimensions citations
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Chapter title
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Chapter number 14
Book title
Machine Learning Meets Quantum Physics
Published in
arXiv, January 2020
DOI 10.1007/978-3-030-40245-7_14
Book ISBNs
978-3-03-040244-0, 978-3-03-040245-7
Authors

Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko, Sauceda, Huziel E., Chmiela, Stefan, Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Researcher 9 19%
Student > Master 5 10%
Student > Bachelor 4 8%
Other 3 6%
Other 4 8%
Unknown 9 19%
Readers by discipline Count As %
Chemistry 19 40%
Physics and Astronomy 7 15%
Materials Science 5 10%
Chemical Engineering 3 6%
Computer Science 3 6%
Other 3 6%
Unknown 8 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 20 September 2019.
All research outputs
#7,092,206
of 25,654,566 outputs
Outputs from arXiv
#117,399
of 935,416 outputs
Outputs of similar age
#146,171
of 479,089 outputs
Outputs of similar age from arXiv
#3,424
of 20,924 outputs
Altmetric has tracked 25,654,566 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 935,416 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 87% 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 479,089 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 69% of its contemporaries.
We're also able to compare this research output to 20,924 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.

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