Theoretical Foundations of Multiscale Modelling
Autor Michele Cascella, Raffaello Potestioen Limba Engleză Paperback – iun 2025
Drawing on the experience of its expert authors, this book is an insightful guide for all those learning, applying or interested in exploring multiscale modelling methods for their own work. Multiscale modelling approaches can provide valuable insights to researchers across a broad range of fields, but the interdisciplinary applicability of these approaches means both new learners and experienced researchers may not have a clear understanding of the theoretical and computational fundamentals underpinning these methods.
- Follows a pedogeological path, taking readers from the simplest to the most complex notions
- Rigorously shows the connections between theory and modeling, providing a critical understanding of commonly used models and their level of applicability
- Provides a rich list of case studies to demonstrate the application of theoretical principles in practice
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Specificații
ISBN-13: 9780323884402
ISBN-10: 0323884407
Pagini: 420
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0323884407
Pagini: 420
Ilustrații: Approx. 100 illustrations
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Public țintă
Students in computational chemistry, condensed matter physics & materials sciences; researchers from across chemistry, physics and materials sciences who use or are interested in using multiscale modelling in their workCuprins
1. Introduction to soft matter
PART I - Statistical mechanics and simulation
2. Foundations of Classical mechanics
3. Foundations of Statistical Mechanics
4. Modelling atomic systems
5. Simulation and sampling
6. Stochastic dynamics
7. Free energy calculations
PART II - Coarse-graining
8. From critical phenomena to coarse-graining
9. Coarse-graining molecular systems
10. Structure and thermodynamics from spatial distribution functions
11. The relative entropy method
12. Boltzmann Inversion, Iterative Boltzmann Inversion and Inverse Monte Carlo
13. Multi-scale coarse-graining and force matching
PART III - Case studies
14. State of the art approaches to multi-scaling
15. Applications: Simple liquids
16. Applications: Macromolecules
PART IV - conclusions and perspectives
17. Conclusions and perspectives
PART I - Statistical mechanics and simulation
2. Foundations of Classical mechanics
3. Foundations of Statistical Mechanics
4. Modelling atomic systems
5. Simulation and sampling
6. Stochastic dynamics
7. Free energy calculations
PART II - Coarse-graining
8. From critical phenomena to coarse-graining
9. Coarse-graining molecular systems
10. Structure and thermodynamics from spatial distribution functions
11. The relative entropy method
12. Boltzmann Inversion, Iterative Boltzmann Inversion and Inverse Monte Carlo
13. Multi-scale coarse-graining and force matching
PART III - Case studies
14. State of the art approaches to multi-scaling
15. Applications: Simple liquids
16. Applications: Macromolecules
PART IV - conclusions and perspectives
17. Conclusions and perspectives