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.
1. Chapter 1 : Very brief introduction to quantum chemistry - p. Page xiii 2. Chapter 2 : Density-functional theory - p. Page 24 3. Chapter 3 : Semiempirical quantum mechanical methods - p. Page 61 4. Chapter 4 : From small molecules to solid-state materials: A brief discourse on an example of carbon compounds - p. Page 90 5. Chapter 5 : Basics of dynamics - p. Page 114 6. Chapter 6 : Machine learning: An overview - p. Page 126 7. Chapter 7 : Unsupervised learning - p. Page 149 8. Chapter 8 : Neural networks - p. Page 178 9. Chapter 9 : Kernel methods - p. Page 203 10. Chapter 10 : Bayesian inference - p. Page 230 11. Chapter 11 : Potentials based on linear models - p. Page 247 12. Chapter 12 : Neural network potentials - p. Page 277 13. Chapter 13 : Kernel method potentials - p. Page 289 14. Chapter 14 : Constructing machine learning potentials with active learning - p. Page 311 15. Chapter 15 : Excited-state dynamics with machine learning - p. Page 325 16. Chapter 16 : Machine learning for vibrational spectroscopy - p. Page 350 17. Chapter 17 : Molecular structure optimizations with Gaussian process regression - p. Page 384 18. Chapter 18 : Learning electron densities - p. Page 424 19. Chapter 19 : Learning dipole moments and polarizabilities - p. Page 450 20. Chapter 20 : Learning excited-state properties - p. Page 464 21. Chapter 21 : Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond - p. Page 486 22. Chapter 22 : Data-driven acceleration of coupled-cluster and perturbation theory methods - p. Page 506 23. Chapter 23 : Redesigning density functional theory with machine learning - p. Page 528 24. Chapter 24 : Improving semiempirical quantum mechanical methods with machine learning - p. Page 553 25. Chapter 25 : Machine learning wavefunction - p. Page 573 26. Chapter 26 : Analysis of nonadiabatic molecular dynamics trajectories - p. Page 613 27. Chapter 27 : Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived quantities - p. Page 649 28. Index - p. Page 668
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.
1. Chapter 1 : Very brief introduction to quantum chemistry - p. Page xiii 2. Chapter 2 : Density-functional theory - p. Page 24 3. Chapter 3 : Semiempirical quantum mechanical methods - p. Page 61 4. Chapter 4 : From small molecules to solid-state materials: A brief discourse on an example of carbon compounds - p. Page 90 5. Chapter 5 : Basics of dynamics - p. Page 114 6. Chapter 6 : Machine learning: An overview - p. Page 126 7. Chapter 7 : Unsupervised learning - p. Page 149 8. Chapter 8 : Neural networks - p. Page 178 9. Chapter 9 : Kernel methods - p. Page 203 10. Chapter 10 : Bayesian inference - p. Page 230 11. Chapter 11 : Potentials based on linear models - p. Page 247 12. Chapter 12 : Neural network potentials - p. Page 277 13. Chapter 13 : Kernel method potentials - p. Page 289 14. Chapter 14 : Constructing machine learning potentials with active learning - p. Page 311 15. Chapter 15 : Excited-state dynamics with machine learning - p. Page 325 16. Chapter 16 : Machine learning for vibrational spectroscopy - p. Page 350 17. Chapter 17 : Molecular structure optimizations with Gaussian process regression - p. Page 384 18. Chapter 18 : Learning electron densities - p. Page 424 19. Chapter 19 : Learning dipole moments and polarizabilities - p. Page 450 20. Chapter 20 : Learning excited-state properties - p. Page 464 21. Chapter 21 : Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond - p. Page 486 22. Chapter 22 : Data-driven acceleration of coupled-cluster and perturbation theory methods - p. Page 506 23. Chapter 23 : Redesigning density functional theory with machine learning - p. Page 528 24. Chapter 24 : Improving semiempirical quantum mechanical methods with machine learning - p. Page 553 25. Chapter 25 : Machine learning wavefunction - p. Page 573 26. Chapter 26 : Analysis of nonadiabatic molecular dynamics trajectories - p. Page 613 27. Chapter 27 : Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived quantities - p. Page 649 28. Index - p. Page 668
