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