(Ebook PDF) Quantum Chemistry in the Age of Machine Learning 1st edition by Pavlo Dral ISBN 9780323886048 0323886043 full chapters

Part 1: Introduction

Chapter 1: Very brief introduction to quantum chemistry

• Abstract
• Acknowledgments
• Introduction—The foundations of quantum chemistry
• Methods of molecular electronic structure computations
• Methods of conceptional interpretation based on electronic structure calculations
• Case studies
• Conclusions and outlook
• References

Chapter 2: Density-functional theory

• Abstract
• Acknowledgments
• Introduction
• Theoretical foundations of DFT
• Density-functional approximations
• Practical aspects of DFT implementations
• Case studies
• Concluding remarks
• References

Chapter 3: Semiempirical quantum mechanical methods

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 4: From small molecules to solid-state materials: A brief discourse on an example of carbon compounds

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 5: Basics of dynamics

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 6: Machine learning: An overview

• Abstract
• Acknowledgment
• Introduction
• Methods
• Basic concepts of machine learning
• Case study
• Conclusions and outlook
• References
• Further reading

Chapter 7: Unsupervised learning

• Abstract
• Introduction
• Notation guide
• Methods
• Case studies
• Conclusions
• References

Chapter 8: Neural networks

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case study
• Conclusions and outlook
• References

Chapter 9: Kernel methods

• Abstract
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 10: Bayesian inference

• Abstract
• Acknowledgments
• Introduction
• Basic concepts of Bayesian statistics
• Bayesian regression
• Bayesian inference in machine learning: Bayesian neural networks
• Case study
• Conclusions and outlook
• References

Part 2: Machine learning potentials

Chapter 11: Potentials based on linear models

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusion and outlook
• References

Chapter 12: Neural network potentials

• Abstract
• Acknowledgment
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 13: Kernel method potentials

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case study
• Conclusions and outlook
• References

Chapter 14: Constructing machine learning potentials with active learning

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case study
• Conclusion and outlook
• References

Chapter 15: Excited-state dynamics with machine learning

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 16: Machine learning for vibrational spectroscopy

• Abstract
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 17: Molecular structure optimizations with Gaussian process regression

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Part 3: Machine learning of quantum chemical properties

Chapter 18: Learning electron densities

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 19: Learning dipole moments and polarizabilities

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 20: Learning excited-state properties

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Part 4: Machine learning-improved quantum chemical methods

Chapter 21: Learning from multiple quantum chemical methods: Δ-learning, transfer learning, co-kriging, and beyond

• Abstract
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 22: Data-driven acceleration of coupled-cluster and perturbation theory methods

• Abstract
• Acknowledgment
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Chapter 23: Redesigning density functional theory with machine learning

• Abstract
• Introduction
• Methods
• Case study
• Conclusions and outlook
• References

Chapter 24: Improving semiempirical quantum mechanical methods with machine learning

• Abstract
• Acknowledgments
• Introduction
• Methods
• Case study
• Conclusions and outlook
• References
• Chapter 25: Machine learning wavefunction
• Abstract
• Acknowledgments
• Introduction
• Methods
• Case studies
• Conclusions and outlook
• References

Part 5: Analysis of big data

Chapter 26: Analysis of nonadiabatic molecular dynamics trajectories

• Abstract
• Introduction
• Theoretical methods
• Examples
• Case studies
• Conclusions and outlook
• References

Chapter 27: Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived quantities

• Abstract
• Introduction
• Methods
• Case studies: Implementing the rational design protocol
• Conclusions and outlook
• References
• Index