Book “Quantum Chemistry in the Age of Machine Learning”
The book “Quantum Chemistry in the Age of Machine Learning” guides aspiring beginners and specialists in this exciting field by covering topics ranging from basic concepts to comprehensive methodological details in machine learning, quantum chemistry, and their combinations in a single, interconnected resource. Each chapter comes with hands-on tutorials, codes, and other materials to deepen understanding of the topics. The book is a product of a massive collaborative effort of 65 authors bringing together their diverse expertise. It has been published on 16 September 2022.
Machine learning (ML) has emerged as an important tool for quantum chemistry (QC) and booming applications of ML in QC necessitate a single resource that can be used for both teaching and as a look-up reference for specialists. With this in mind, the book 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. It also provides plenty of material for teaching and each chapter contains hands-on tutorials in the Case studies section.
The work on this book started in 2020 as an in-depth extension of my same-title Perspective providing a concise, bird-eye view of ML in QC. My biggest motivation to embark on such a big project was creating a textbook for teaching ML in QC courses and, indeed, many of my lecture notes and exercises turned out to be very useful when contributing to some of the chapters. Considering the broadness of topics, this project was obviously too big to accomplish by a single person within a reasonably short amount of time adequate for such a fast-paced field. I am very grateful to many excellent experts in their fields who enthusiastically rolled up their sleeves and created this book. Also, the contribution of the publisher’s team and all the people who peer-reviewed chapters, checked hands-on tutorials and provided suggestions and feedback was invaluable.
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://www.elsevier.com/books-and-journals/book-companion/9780323900492
- Mirror website to be updated more regularly and to host any additional information (such as preprints of chapters) https://github.com/dralgroup/MLinQCbook22
The table of content is below:
| 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
Apply promo code ATR30 during checkout for 30% discount.
As an Editor and co-author of this book, I wish you happy reading!
0 Comments on “Book “Quantum Chemistry in the Age of Machine Learning””