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Galbraith / Crawford

Introduction to Computational Chemistry

Methods and Applications

Medium: Buch
ISBN: 978-0-443-29921-6
Verlag: Elsevier Science
Erscheinungstermin: 01.05.2026
vorbestellbar, Erscheinungstermin ca. Juni 2026

Introduction to Computational Chemistry provides a foundational, introductory overview of this critical and important field designed to give students a clear and supportive pathway. It is intended to be a non-mathematics heavy introduction to the methods used in computational chemistry, together with information about how HPC-style computers are set up and utilized for performing calculations. It also provides novel insight into the computational chemist mentality: sometimes, the way computational chemists operate can seem strange to someone not yet immersed in the field. The book starts with a basic discussion of computer functionality through operating systems, system administration, and programming followed by a look at the key computational methods for electronic structure methods and molecular mechanics, hybrid methods, and solid-state materials. For each subject, essential non-mathematical information is first provided so that the reader can immediately begin to effectively use computational chemistry software. This introductory material is followed by a section that provides more theoretical information and then references for the reader wishing to go much deeper. Often this type of book overloads the reader with too much information; this one is set up in such a way as to quickly present essential information regarding the fundamental approaches and applications of computational chemistry to beginners in a down to earth and uncluttered manner, while providing the means and resources for more advanced readers to explore further. Introduction to Computational Chemistry is written primarily for upper level undergraduate and entry level graduate students completely new to the field of computational chemistry, with little background knowledge; the book is well suited to entry level courses at this level.


Produkteigenschaften


  • Artikelnummer: 9780443299216
  • Medium: Buch
  • ISBN: 978-0-443-29921-6
  • Verlag: Elsevier Science
  • Erscheinungstermin: 01.05.2026
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2026
  • Produktform: Kartoniert
  • Seiten: 456
  • Format (B x H): 191 x 235 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

John M. Galbraith is a Computational Chemist with more than 30 years' experience in molecular orbital and valence bond theory calculations of small molecules. He has 20+ years teaching undergraduate courses in Computational, Physical, Inorganic, and General Chemistry. He is currently an Associate Professor of Chemistry at the Department of Chemistry, Biochemistry, and Physics, Marist University, USA.

T. Daniel Crawford is University Distinguished Professor of Chemistry at Virginia Tech, USA and the Director of the Molecular Sciences Software Institute in Blacksburg, Virginia. He received his bachelor's degree in 1992 from Duke University and his Ph.D. in 1996 from the University of Georgia, working with Prof. Fritz Schaefer. His research focuses on quantum chemical models of molecular properties, particularly the spectroscopic responses of chiral compounds. He is a Fellow of the American Chemical Society and the winner of 2010 Dirac Medal of the World Association of Theoretical and Computational Chemists.

Section I. Computers
1. System administration and operating systems
2. Math Packages
3. Programming

Section II. Electronic Structure Methods
4. Basis sets
5. Molecular Orbital Methods
6. Valence Bond Methods
7. Density Functional Methods
8. Semi-empirical methods
9. Applications
10. Data analysis

Section III. Molecular Mechanics Methods
11. Force Field models
12. Applications

Section IV. Hybrid Methods
13. QM/MM
14. Empirical Valence Bond Methods

Section V. Solids and Surfaces:

15. Periodic systems
16. Applications

Section VI. Simulation Techniques
17. Molecular Dynamics Methods
18. Monte Carlo Methods
19. Applications

Section VII. Large Data Sets

20. Machine Learning

Section VIII. Resources

21. MolSSI
22. Computer resources
23. Software resources