Comprehensive coverage of advanced Python programming paradigms for quantum chemistry applications enables readers to leverage multiple programming styles for their projects
In-depth exploration of Python optimization techniques allows users to accelerate their computational code to near-C performance
Covers data analysis tools like NumPy and Pandas for efficient management and analysis of computational chemistry datasets
Includes practical examples of Python applications using cutting-edge technologies such as automatic code generation, cloud computing, and GPU acceleration
Guides readers through best practices for code organization, version control, and project reuse in scientific computing environments
Summarized by Shop
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
Format
Book
Target Audience
Graduate students, researchers, software engineers in theoretical chemistry, computational chemistry, condensed matter physics, material modelling, molecular simulations, and quantum computing