Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry (ePub)
(Sprache: Englisch)
Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry outlines important theories and algorithms of DMRG-based approaches and explores their use in computational chemistry. Beginning with an introduction to DMRG and...
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Density Matrix Renormalization Group (DMRG)-based Approaches in Computational Chemistry outlines important theories and algorithms of DMRG-based approaches and explores their use in computational chemistry. Beginning with an introduction to DMRG and DMRG-based approaches, the book goes on to discuss the key theories and applications of DMRG, from DMRG for semi-empirical and ab-initio quantum chemistry, to DMRG in embedded environments, frequency spaces and quantum dynamics. Drawing on the experience of its expert authors, sections detail recent ideas and key developments, providing an up-to-date view of current developments in the field for students and researchers in quantum chemistry.
- Provides an expertly-curated, consolidated overview of research in the field
- Includes exercises that support learning and link theory to practice
- Outlines key theories and algorithms for computational chemistry applications
Autoren-Porträt von Haibo Ma, Ulrich Schollwöck, Zhigang Shuai
Haibo Ma is Professor of Theoretical Chemistry at Nanjing University. He has a B.S. and a Ph.D. from Nanjing University in 2002 and 2007 respectively. He has received the Humboldt research Fellowship from the Alexander von Humboldt foundation (2007-2009) and Tang Au-Qing youth award on theoretical chemistry from Chinese chemical society (2018). His main research interests focus on the development and implementation of renormalization group-based quantum chemical methods as well as their applications in the study of strongly correlated systems and excited state processes.
Bibliographische Angaben
- Autoren: Haibo Ma , Ulrich Schollwöck , Zhigang Shuai
- 2022, 336 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 0323856950
- ISBN-13: 9780323856959
- Erscheinungsdatum: 21.08.2022
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- Dateiformat: ePub
- Größe: 12 MB
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Sprache:
Englisch
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