Quantum Chemistry in the Age of Machine Learning

Quantum Chemistry in the Age of Machine Learning

1st Edition - September 1, 2022

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  • Editor: Pavlo Dral
  • Paperback ISBN: 9780323900492

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Description

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.

Key Features

  • 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

Readership

Advanced students, early-career and senior scientists in the fields of computational, theoretical, and applied chemistry

Table of Contents

  • Part 1: Introduction
    1. Very brief introduction to quantum chemistry
    2. Density functional theory
    3. Semiempirical methods
    4. Basics of molecular dynamics
    5. From small molecules to materials science
    6. Machine learning: An Overview
    7. Supervised learning: Neural networks
    8. Supervised learning: Kernel methods
    9. Bayesian inference
    10. Active learning
    11. Unsupervised learning

    Part 2: Machine learning potentials
    12. Potentials based on linear models
    13. Dynamics with neural-network-based potentials
    14. Dynamics with Gaussian process regression potentials
    15. Dynamics with kernel ridge regression potentials
    16. Excited-state dynamics
    17. Vibrational spectra with machine learning
    18. Finding critical points on potential energy surface: Geometry optimizations and transition state search

    Part 3: Machine learning of quantum chemical properties
    19. Learning of electron densities
    20. Learning of dipole moments
    21. Learning of excited-state properties
    22. Learning of other properties

    Part 4: Machine learning-improved quantum chemical methods
    23. Machine learning for accelerating and improving ab initio wavefunction-based methods
    24. Redesigning density functional theory with machine learning
    25. Improved semiempirical Hamiltonians with machine learning
    26. Δ-learning and beyond
    27. Machine learning wavefunction

    Part 5: Analysis of Big Data
    28. Analysis of dynamics trajectories
    29. Insights for rational materials design

Product details

  • No. of pages: 560
  • Language: English
  • Copyright: © Elsevier 2022
  • Published: September 1, 2022
  • Imprint: Elsevier
  • Paperback ISBN: 9780323900492

About the Editor

Pavlo Dral

Pavlo O. Dral is Associate Professor at Xiamen University, PR China. 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 the MLatom, a program package for atomistic machine learning. His more than 40 publications were cited over 2600 times and his h-index is 21 (Google Scholar, Spring 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 and postdoc with Prof. Walter Thiel at the Max Planck Institute for Coal Research in 2013–2019. More information is available on Dral’s group website dr-dral.com.

Affiliations and Expertise

Pavlo O. Dral is Associate Professor at Xiamen University, PR China.

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