
Chemical Modelling
Volume 17
Royal Society of Chemistry (Publisher)
Published on 19. December 2022
Book
Hardback
216 pages
978-1-83916-741-6 (ISBN)
Description
Chemical modelling covers a wide range of disciplines, and this book is the first stop for any chemist, materials scientist, biochemist, or molecular physicist wishing to acquaint themselves with major developments in the applications and theory of chemical modelling. Containing both comprehensive and critical reviews, it is a convenient reference to the current literature. Coverage includes, but is not limited to, considerations towards rigorous foundations for the natural-orbital representation of molecular electronic transitions, quantum and classical embedding schemes for optical properties, machine learning for excited states, ultrafast and wave function-based electron dynamics, and attosecond chemistry.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 18 mm
Weight
1334 gr
ISBN-13
978-1-83916-741-6 (9781839167416)
DOI
10.1039/9781839169342
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
12/2022
1st Edition
Royal Society of Chemistry
€432.99
Available for download

E-Book
12/2022
1st Edition
Royal Society of Chemistry
€432.99
Available for download
Persons
Editor
Wuppertal University, Germany
Freie Universitaet Berlin, Germany
Content
Towards Predictive Computational Catalysis - A Case Study of Olefin Metathesis with Mo Imido Alkylidene N-heterocyclic Carbene Catalysts;Quantum-derived Embedding Schemes for Local Excitations;Natural-orbital Representation of Molecular Electronic Transitions;Developing Electron Dynamics into a Tool for 21st Century Chemistry Simulations;Recent Advances in Theoretical Attosecond Chemistry;Recent Advances in Machine Learning for Electronic Excited State Molecular Dynamics Simulations