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Machine Learning in Molecular Sciences

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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 5: Machine-Learning for Static and Dynamic Electronic Structure Theory
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