Quantum Chemistry in the Age of Machine Learning
- 1st Edition - September 15, 2022
- Imprint: Elsevier
- Editor: Pavlo O. Dral
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 0 4 9 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 8 6 0 4 - 8
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 te… Read more
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Request a sales quoteQuantum 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
Advanced students, early-career and senior scientists in the fields of computational, theoretical, and applied chemistry
- Cover image
- Title page
- Table of Contents
- Copyright
- Companion website
- Contributors
- Preface
- Reference
- 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
- Edition: 1
- Published: September 15, 2022
- Imprint: Elsevier
- No. of pages: 698
- Language: English
- Paperback ISBN: 9780323900492
- eBook ISBN: 9780323886048
PD
Pavlo O. Dral
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.
Affiliations and expertise
Pavlo O. Dral is Full Professor at Xiamen University, PR China.Read Quantum Chemistry in the Age of Machine Learning on ScienceDirect