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Machine Learning Meets Quantum Physics

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Cover of 'Machine Learning Meets Quantum Physics'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Introduction to Material Modeling
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    Chapter 3 Kernel Methods for Quantum Chemistry
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    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
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    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
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    Chapter 17 Database-Driven High-Throughput Calculations and Machine Learning Models for Materials Design
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    Chapter 18 Polymer Genome: A Polymer Informatics Platform to Accelerate Polymer Discovery
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    Chapter 19 Bayesian Optimization in Materials Science
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    Chapter 20 Recommender Systems for Materials Discovery
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    Chapter 21 Generative Models for Automatic Chemical Design
Attention for Chapter 10: Message Passing Neural Networks
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Chapter title
Message Passing Neural Networks
Chapter number 10
Book title
Machine Learning Meets Quantum Physics
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-40245-7_10
Book ISBNs
978-3-03-040244-0, 978-3-03-040245-7
Authors

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

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 %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 20%
Lecturer > Senior Lecturer 1 10%
Professor 1 10%
Student > Ph. D. Student 1 10%
Student > Master 1 10%
Other 2 20%
Unknown 2 20%
Readers by discipline Count As %
Computer Science 3 30%
Engineering 3 30%
Materials Science 2 20%
Unknown 2 20%