Jun Li, Hongwei Song, Yongle Li

Broschiertes Buch

Theoretical and Computational Advances of Molecular Reaction Dynamics

Beschreibung

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. It 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. Useful and timely tutorials for the practical implementations with tips and usable codes are included for research in this multidisciplinary field. The book also familiarizes readers with state-of-the-art research frontiers on theoretical and computational MRD, showing the new methods, theories, applications, and advances that have developed for the investigation of chemical kinetics, reaction dynamics, and molecular spectra-at the microscopic atomic or molecular level-in the era of machine learning and big data.

Autor

Jun Li is Professor of Chemistry at the School of Chemistry and Chemical Engineering, Chongqing University, PR China. He received his Bachelor and Doctoral degrees from Sichuan University. He worked with Professor Sheng-Hsien Lin as a visiting student at National Chiao-Tung University, PR China on photochemistry for five months. After a three-year postdoctoral research role with Prof. Hua Guo at University of New Mexico, USA, he joined Chongqing University as an independent PI. With a Humboldt Fellowship for Experienced Researchers, he then worked with Professor Jörg Behler at Georg-August-Universität Göttingen, Germany. He has published more than 120 peer-reviewed articles and 2 book chapters "Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Small Molecular Systems? by Jun Li*, Yang Liu, in the book entitled Machine Learning in Molecular Sciences (Springer Nature, 2023) and "Tunneling in unimolecular and bimolecular reactions? by Hua Guo, Jianyi Ma, and Jun Li in Molecular Quantum Dynamics: From Theory to Applications (Physical Chemistry in Action), Edited by Fabien Gatti, Springer 2014. His research interests include potential energy surfaces, reaction kinetics, dynamics, and atomic-level mechanisms, mainly for gas phase systems, and machine learning.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 352
• Erscheinungstermin: 1. August 2025
• Englisch
• ISBN-13: 9780443137990
• ISBN-10: 0443137994
• Artikelnr.: 72059897