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Machine Learning in Molecular Sciences

Overview of attention for book
Cover of 'Machine Learning in Molecular Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Machine Learning in Molecular Sciences
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    Chapter 2 Graph Neural Networks for Molecules
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    Chapter 3 Voxelized Representations of Atomic Systems for Machine Learning Applications
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    Chapter 4 Development of Exchange-Correlation Functionals Assisted by Machine Learning
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    Chapter 5 Machine-Learning for Static and Dynamic Electronic Structure Theory
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    Chapter 6 Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-Dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions
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    Chapter 7 Machine Learning Applications in Chemical Kinetics and Thermochemistry
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    Chapter 8 Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis
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    Chapter 9 Machine Learning for Protein Engineering
Attention for Chapter 6: Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-Dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions
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mendeley
2 Mendeley
Summary
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Chapter title
Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-Dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions
Chapter number 6
Book title
Machine Learning in Molecular Sciences
Published by
Springer, Cham, January 2023
DOI 10.1007/978-3-031-37196-7_6
Book ISBNs
978-3-03-137195-0, 978-3-03-137196-7
Authors

Li, Jun, Liu, Yang

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 100%
Readers by discipline Count As %
Chemistry 2 100%

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