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.
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Table of Contents
1. Very brief introduction to quantum chemistry2. Density functional theory
3. Semiempirical quantum mechanical methods
4. From small molecules to solid-state materials: A brief discourse on an example of carbon compounds
5. Basics of dynamics
6. Machine learning: An overview
7. Unsupervised learning
8. Neural networks
9. Kernel methods
10. Bayesian inference
11. Potentials based on linear models
12. Neural network potentials
13. Kernel method potentials
14. Constructing machine learning potentials with active learning
15. Excited-state dynamics with machine learning
16. Machine learning for vibrational spectroscopy
17. Molecular structure optimizations with Gaussian process regression
18. Learning electron densities
19. Learning dipole moments and polarizabilities
20. Learning excited-state properties
21. Learning from multiple quantum chemical methods: ?-learning, transfer learning, co-kriging, and beyond
22. Data-driven acceleration of coupled-cluster and perturbation theory methods
23. Redesigning density functional theory with machine learning
24. Improving semiempirical quantum mechanical methods with machine learning
25. Machine learning wavefunction
26. Analysis of nonadiabatic molecular dynamics trajectories
27. Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived Quantities
