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New Book Published: “Quantum Chemistry in the Age of Machine Learning”

Posted：2022-10-05  Visits：



Our lab’s Prof. Pavlo O. Dral served as an Editor and co-author of the book “Quantum Chemistry in the Age of Machine Learning” published by Elsevier on 16th September, 2022. Professors Peifeng Su, Gang Fu, and Yi Zhao of our lab also contributed to chapters in this book. The work on this book started in 2020 as an in-depth extension of the same-title concise Perspective by Prof. Pavlo O. Dral [J. Phys. Chem. Lett. 2020, 11, 2336–2347], and significant portions of the book are based on his teaching materials.

Machine learning (ML) has emerged as an important tool for quantum chemistry (QC) and booming applications of ML in QC profoundly change the research and scope of quantum chemistry and even entire chemistry. The book is a product of a massive international collaborative effort of 65 authors bringing together their diverse expertise. The content covers a wide variety of topics relevant to ML in QC: 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. The book also provides plenty of material for teaching to deepen understanding of each chapter's content and facilitate self-study. Each chapter has practical tutorials in the Case Study part, and some chapters are based on the lecture notes and exercises taught by Prof. Pavlo O. Dral in Xiamen University.

The book brings together the scientific research results of experts at the forefront of international research in recent years. It serves as an important reference and a guide for both aspiring beginners and specialists in this exciting field.

The content of this book and the authors of each chapter are as follows:

Chapter

Title

Authors

 

Preface

Pavlo O. Dral

Part 1

Introduction

 

1

Very brief introduction to quantum chemistry

Xun Wu, Peifeng Su

2

Density functional theory

Hong Jiang, Huai-Yang Sun

3

Semiempirical quantum mechanical methods

Pavlo O. Dral, Jan Řezáč

4

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

Bili Chen, Leyuan Cui, Shuai Wang, Gang Fu

5

Basics of dynamics

Xinxin Zhong, Yi Zhao

6

Machine learning: An overview

Eugen Hruska, Fang Liu

7

Unsupervised learning

Rose K. Cersonsky, Sandip De

8

Neural networks

Pavlo O. Dral, Alexei Kananenka, Fuchun Ge, Bao-Xin Xue

9

Kernel methods

Max Pinheiro Jr, Pavlo O. Dral

10

Bayesian inference

Wei Liang, Hongsheng Dai

Part 2

Machine learning potentials

 

11

Potentials based on linear models

Gauthier Tallec, Gaétan Laurens, Owen Fresse–Colson, Julien Lam

12

Neural network potentials

Jinzhe Zeng, Liqun Cao, Tong Zhu

13

Kernel method potentials

Yi-Fan Hou, Pavlo O. Dral

14

Constructing machine learning potentials with active learning

Cheng Shang, Zhi-Pan Liu

15

Excited-state dynamics with machine learning

Lina Zhang, Arif Ullah, Max Pinheiro Jr, Mario Barbatti, Pavlo O. Dral

16

Machine learning for vibrational spectroscopy

Sergei Manzhos, Manabu Ihara, Tucker Carrington

17

Molecular structure optimizations with Gaussian process regression

Roland Lindh, Ignacio Fernández Galván

Part 3

Machine learning of quantum chemical properties

 

18

Learning electron densities

Bruno Cuevas-Zuviría

19

Learning dipole moments and polarizabilities

Yaolong Zhang, Jun Jiang, Bin Jiang

20

Learning excited-state properties

Julia Westermayr, Pavlo O. Dral, Philipp Marquetand

Part 4

Machine learning-improved quantum chemical methods

 

21

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

Pavlo O. Dral, Tetiana Zubatiuk, Bao-Xin Xue

22

Data-driven acceleration of coupled-cluster and perturbation theory methods

Grier M. Jones, P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis

23

Redesigning density functional theory with machine learning

Jiang Wu, Guanhua Chen, Jingchun Wang, Xiao Zheng

24

Improving semiempirical quantum mechanical methods with machine learning

Pavlo O. Dral, Tetiana Zubatiuk

25

Machine learning wavefunction

Stefano Battaglia

Part 5

Analysis of Big Data

 

26

Analysis of nonadiabatic molecular dynamics trajectories

Yifei Zhu, Jiawei Peng, Hong Liu and Zhenggang Lan

27

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

Gaurav Vishwakarma, Aditya Sonpal, Aatish Pradhan, Mojtaba
Haghighatlari, Mohammad Atif Faiz Afzal, Johannes Hachmann

 

Link to the book “Quantum Chemistry in the Age of Machine Learning”:
https://www.elsevier.com/books/quantum-chemistry-in-the-age-of-machine-learning/dral/978-0-323-90049-2


Mirror website to be updated more regularly and to host any additional information (such as preprints of chapters)

https://www.elsevier.com/books-and-journals/book-companion/9780323900492

 

The book is accompanied with a companion site hosting links to repositories with programs, data, instructions, sample input, and output files required for hands-on tutorials (case studies) as well as any post-publication updates:

https://github.com/dralgroup/MLinQCbook22