Quantum Chemistry in the Age of Machine Learning
Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry
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Quantum Chemistry in the Age of Machine Learning
Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry
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Quantum Chemistry in the Age of Machine Learning

Quantum Chemistry in the Age of Machine Learning

Quantum Chemistry in the Age of Machine Learning
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Quantum Chemistry in the Age of Machine Learning

Quantum Chemistry in the Age of Machine Learning

Overview

Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field. - Compiles advances of machine learning in quantum chemistry across different areas into a single resource - Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry - Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry

Product Details

ISBN-13: 9780323886048
Publisher: Elsevier Science
Publication date: 09/16/2022
Sold by: Barnes & Noble
Format: eBook
Pages: 698
File size: 69 MB
Note: This product may take a few minutes to download.

About the Author

Pavlo O. Dral is Full Professor at Xiamen University. He is a specialist in accelerating and improving quantum chemistry with artificial intelligence/machine learning. Together with his colleagues, he introduced and continues to develop methods such as Δ-learning, AIQM1, fourdimensional spacetime atomistic artificial intelligence models, and artificial intelligence-based quantum dynamics. Pavlo Dral is also a founder of MLatom, a program package for atomistic machine learning, and a co-founder of the Xiamen Atomistic Computing Suite. His more than 40 publications were cited over 2800 times and his h-index is 22 (Google Scholar, Summer 2022). Pavlo O. Dral has won a gold medal in the 36th International Chemistry Olympiad, 2004. He did his PhD with Prof. Tim Clark at the University of Erlangen–Nuremberg in 2010–2013, postdoc with Prof. Walter Thiel at the Max Planck Institute for Coal Research in 2013–2019, and began his independent career at Xiamen University in 2019 first as an Associate Professor and from 2021 as a Full Professor. More information is available on Dral’s group website dr-dral.com.

Table of Contents

1. Very brief introduction to quantum chemistry2. Density functional theory3. Semiempirical quantum mechanical methods4. From small molecules to solid-state materials: A brief discourse on an example of carbon compounds5. Basics of dynamics6. Machine learning: An overview7. Unsupervised learning8. Neural networks9. Kernel methods10. Bayesian inference11. Potentials based on linear models12. Neural network potentials13. Kernel method potentials14. Constructing machine learning potentials with active learning15. Excited-state dynamics with machine learning16. Machine learning for vibrational spectroscopy17. Molecular structure optimizations with Gaussian process regression18. Learning electron densities19. Learning dipole moments and polarizabilities20. Learning excited-state properties21. Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond22. Data-driven acceleration of coupled-cluster and perturbation theory methods23. Redesigning density functional theory with machine learning24. Improving semiempirical quantum mechanical methods with machine learning25. Machine learning wavefunction26. Analysis of nonadiabatic molecular dynamics trajectories27. Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived Quantities

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An informative guide to developments in quantum chemistry assisted by machine learning

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