Our college’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 college 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 our college.

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:

Link to the book:
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