Building on the power of machine learning approaches, this guide shows how chemical space may be explored with computational experiments that can complement or replace empirical studies.
Sprache
Verlagsort
Zielgruppe
Illustrationen
100 s/w Abbildungen, 100 farbige Abbildungen
Maße
Höhe: 244 mm
Breite: 170 mm
ISBN-13
978-3-527-35110-7 (9783527351107)
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Schweitzer Klassifikation
Markus Reiher received his PhD in theoretical chemistry from the University of Bielefeld in 1998, working in the group of Juergen Hinze. After his habilitation in the group of Bernd Artur Hess at the University of Erlangen, he worked as a private docent at the same university in 2003 and at the University of Bonn in 2004. In 2005, he accepted an offer for a professorship in physical chemistry at the University of Jena, where he worked until he moved to ETH Zurich in 2006. His research covers many different areas in theoretical chemistry which range from relativistic quantum chemistry to the development of new electron-correlation theories, quantum computing, and smart algorithms for the efficient exploration of complex chemical reaction networks. He has authored more than 300 scientific papers and published a monograph on relativistic quantum chemistry which has become a standard textbook on the topic.
Enumeration and taxonomy of chemical space
Striding across chemical compound space with machine learning
The subspace of transition-metal compounds
Materials space
Synthesis planning through artifical intelligence
Autonomous discovery systems through robotics
Chemical reaction space
Graph-based approaches for reaction networks
Artificial intellience in reaction space exploration
Heterogeneous systems (reactions on surfaces)
Astrochemical reaction networks
Deep space of combustion chemistry
Kinetics of complex reacting systems
The nanoreactor
Explorative molecular dynamics
Sampling conformational space
Virtual reality for chemical explorations