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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
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    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
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    Chapter 7 Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches
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    Chapter 8 Quantum Machine Learning with Response Operators in Chemical Compound Space
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    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
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    Chapter 15 Active Learning and Uncertainty Estimation
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    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 13: High-Dimensional Neural Network Potentials for Atomistic Simulations
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
69 Dimensions

Readers on

mendeley
10 Mendeley
Summary X Dimensions citations
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Chapter title
High-Dimensional Neural Network Potentials for Atomistic Simulations
Chapter number 13
Book title
Machine Learning Meets Quantum Physics
Published in
arXiv, January 2020
DOI 10.1007/978-3-030-40245-7_13
Book ISBNs
978-3-03-040244-0, 978-3-03-040245-7
Authors

Michael Heller, Matti Hellström, Jörg Behler, Hellström, Matti, Behler, Jörg

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 1 10%
Other 1 10%
Professor 1 10%
Student > Ph. D. Student 1 10%
Student > Master 1 10%
Other 2 20%
Unknown 3 30%
Readers by discipline Count As %
Physics and Astronomy 3 30%
Materials Science 2 20%
Chemistry 2 20%
Unknown 3 30%
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 27 January 2020.
All research outputs
#16,082,858
of 23,858,780 outputs
Outputs from arXiv
#399,907
of 993,285 outputs
Outputs of similar age
#279,588
of 461,225 outputs
Outputs of similar age from arXiv
#11,579
of 27,709 outputs
Altmetric has tracked 23,858,780 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 993,285 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 52% 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 461,225 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27,709 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.

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