Theoretical and Computational Advances of Molecular Reaction Dynamics

Theoretical and Computational Advances of Molecular Reaction Dynamics provides a strong foundation and comprehensive review of the principles, formulations, and methodology of MRD, with detailed tutorial guides and case studies for practical application, whilst demonstrating recent developments. The book is designed to help improve understanding of the full-dimension accurate potential of the energy surface, chemical kinetics, reaction dynamics, collision energy transfer, and molecular spectra using MRD techniques. Details are given for calculating various molecular dynamic properties for various prototypical reactions/chemical species efficiently and accurately; these are then analysed, discussed, and/or compared to other available experiments. Useful and timely tutorials for the practical implementations with tips and usable codes are included for research in this multidisciplinary field. It also familiarizes the reader with state-of-the-art research frontiers on theoretical and computational MRD, showing the new methods, theories, applications, and advances that have developed for investigation of chemical kinetics, reaction dynamics, molecular spectra, at the microscopic atomic or molecular level, in the era of machine learning and big data. Theoretical and Computational Advances of Molecular Reaction Dynamics is written primarily for advanced undergraduates, graduate students, and researchers in the fields of theoretical chemistry, chemical physics, physical chemistry, and biochemistry, looking at molecular reaction dynamics, chemical kinetics, ultrafast spectroscopy, and related machine learning and relevant big data and AI developments.

• Summarizes recent advances in computational molecular reaction dynamics, offering learning about the connection between molecular reaction dynamics and novel data-driven modeling
• Applies recent advances in classical and quantum dynamics methodologies to deal with polyatomic molecular reactions
• Provides detailed procedures on how to develop an accurate ab initio based potential energy surface for polyatomic systems with the aid of machine learning and big data
• Offers practical sample programs to help readers understand and use molecular reaction dynamic modelling effectively and efficiently
• Displays the progress made due to more accurate treatments for the descriptions of electron and nuclear motions, aided by machine learning and big data