Python for Quantum Chemistry:A Full Stack Programming Guide: Volume 23

Auteur
Qiming Sun is primarily known for his contributions to the field of quantum chemistry, particularly through his work in developing the Python package PySCF. He has experience across multiple technical fields such as machine learning, quantum computing algorithms, and cloud computing. He received a Ph.D. in theoretical chemistry from Peking University, PR China in 2012, where he focused on relativistic theory and quantum chemistry method development, as well as high-performance computing programs written in Fortran and C++. Between 2012 and 2018, he continued his research career in Garnet Chan's group at Princeton University, USA and later moved to Caltech, USA. During this period, he gained knowledge of quantum entanglement and quantum embedding theory which are closely related to quantum computing algorithms. In 2018, he worked in Tencent quantum lab as a principal scientist, where he built up the experiences of machine learning, quantum computing algorithms, and cloud computing. In 2020 he joined hedge fund company Axiomquant LLC as a technology leader, where he is responsible for the cloud infrastructure, trading system, and financial data development.

Around 2014, he began promoting the use of Python in quantum chemistry research and led the development of the widely used Python quantum chemistry package PySCF, which is now utilised by thousands of universities and companies in quantum chemistry, quantum computing, and AI-chemistry research. He remains actively involved in researching and developing new algorithms in the field of quantum chemistry.

Texte du rabat

Quantum chemistry requires ever higher computational performance, with more and more sophisticated and dedicated Python scripts being required to solve challenging problems. Although resources for basic use of Python are widely (and often freely) available online and in literature, truly cohesive materials for advanced Python programming skills are lacking. Qiming Sun, a developer of the popular Python package PySCF, provides a comprehensive, end-to-end practical resource for researchers and engineers who have basic Python programming experiences chiefly in computational chemistry but want to take their use of the software forwards to the next level, the book provides an insightful exploration of Numpy, Pandas, and other data analysis tools. Readers will learn how to manage their Python computational projects in a professional way, with various tools and protocols for computational chemistry research and general scientific computing tasks exhibited and analysed from a technical perspective. Multiple programming paradigms including object-oriented, functional, meta-programming, dynamic, concurrent, and vector-oriented are illustrated in various technology scenarios allowing readers to properly use them to enhance their program projects. Readers will also learn how to use the presented optimization technologies to speed up their Python applications, even to the level as fast as a native C++ implementation. The applications of these technologies are then demonstrated using quantum chemistry Python applications. Python for Quantum Chemistry: A Full Stack Programming Guide is written primarily for graduate students, researchers and software engineers working primarily in the fields of theoretical chemistry, computational chemistry, condensed matter physics, material modelling, molecular simulations, and quantum computing.

Contenu

Part I: Python tools for chemistry research

1. Research environment in Python
2. Data processing
3. Scientific computing tools
4. IO
5. How to communicate with other programs
6. Code generation
7. Workflow and job scheduler

Part II: High performance computing with Python

8. Combining Python with other programming languages
9. Code performance optimization
10. Tensor
11. Parallelism
12. Python with GPU

Part III: Quantum chemistry method development with Python

13. Integral evaluation
14. Numerical optimization methods
15. Mean-filed methods
16. Post-Hartree-Fock methods
17. Molecular properties
18. Symmetry