Current Page: Home > News > Lab News > Content

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:






Pavlo O. Dral

Part 1




Very brief introduction to quantum chemistry

Xun Wu, Peifeng Su


Density functional theory

Hong Jiang, Huai-Yang Sun


Semiempirical quantum mechanical methods

Pavlo O. Dral, Jan Řezáč


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

Bili Chen, Leyuan Cui, Shuai Wang, Gang Fu


Basics of dynamics

Xinxin Zhong, Yi Zhao


Machine learning: An overview

Eugen Hruska, Fang Liu


Unsupervised learning

Rose K. Cersonsky, Sandip De


Neural networks

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


Kernel methods

Max Pinheiro Jr, Pavlo O. Dral


Bayesian inference

Wei Liang, Hongsheng Dai

Part 2

Machine learning potentials



Potentials based on linear models

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


Neural network potentials

Jinzhe Zeng, Liqun Cao, Tong Zhu


Kernel method potentials

Yi-Fan Hou, Pavlo O. Dral


Constructing machine learning potentials with active learning

Cheng Shang, Zhi-Pan Liu


Excited-state dynamics with machine learning

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


Machine learning for vibrational spectroscopy

Sergei Manzhos, Manabu Ihara, Tucker Carrington


Molecular structure optimizations with Gaussian process regression

Roland Lindh, Ignacio Fernández Galván

Part 3

Machine learning of quantum chemical properties



Learning electron densities

Bruno Cuevas-Zuviría


Learning dipole moments and polarizabilities

Yaolong Zhang, Jun Jiang, Bin Jiang


Learning excited-state properties

Julia Westermayr, Pavlo O. Dral, Philipp Marquetand

Part 4

Machine learning-improved quantum chemical methods



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

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


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

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


Redesigning density functional theory with machine learning

Jiang Wu, Guanhua Chen, Jingchun Wang, Xiao Zheng


Improving semiempirical quantum mechanical methods with machine learning

Pavlo O. Dral, Tetiana Zubatiuk


Machine learning wavefunction

Stefano Battaglia

Part 5

Analysis of Big Data



Analysis of nonadiabatic molecular dynamics trajectories

Yifei Zhu, Jiawei Peng, Hong Liu and Zhenggang Lan


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”:

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


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: