Multiscale modelling approaches can provide valuable insight to researchers across a broad range of fields. At the same time, the interdisciplinary applicability of these approaches means that both new learners and experienced researchers may not have a clear understanding of the theoretical and computational fundamentals underpinning these methods. Theoretical Foundations of Multiscale Modelling aims to address this knowledge gap, providing a clear insight to the thermodynamical and statistical mechanical foundations of methods commonly employed, and highlighting the potential and limitations of each approach.
Starting with an overview of the basics of statistical mechanics, the first chapters of the book illustrate the general theory of molecular modelling, the concepts and methods for the study of complex molecular systems, then progressing to cover key strategies employed to perform computational calculations and simulations. The second part of the book reviews how the most recent, cutting-edge tools of the trade are used in practice through a selection of case studies highlighting multiscale modelling, multiple resolution simulation methods, and machine-learning applications. This is also achieved by presenting case studies from recent scientific literature, highlighting the advantages of a multiscale treatment for the understanding of complex molecular phenomena.
Drawing on the experience of its authors, Theoretical Foundations of Multiscale Modelling is an insightful guide for all those learning, applying, or interested in exploring multiscale modelling methods for their work.
1. Chapter 1: Introduction to soft matter - p. 1 2. Chapter 2: Foundations of classical mechanics - p. 19 3. Chapter 3: Foundations of statistical mechanics - p. 39 4. Chapter 4: Structure and thermodynamics of fluids from spatial distribution functions - p. 71 5. Chapter 5: Modelling atomic systems - p. 89 6. Chapter 6: Simulation and sampling - p. 109 7. Chapter 7: Stochastic dynamics - p. 139 8. Chapter 8: Free energy and enhanced sampling - p. 155 9. Chapter 9: From critical phenomena to coarse-graining - p. 189 10. Chapter 10: Coarse-graining molecular systems - p. 217 11. Chapter 11: The relative entropy method - p. 235 12. Chapter 12: Boltzmann inversion, iterative Boltzmann inversion and inverse Monte Carlo - p. 249 13. Chapter 13: Gradient-based methods for multiscale modelling - p. 265 14. Chapter 14: Hybrid multiresolution approaches - p. 291 15. Chapter 15: Concluding remarks - p. 331 16. References - p. 337 17. Index - p. 355
Multiscale modelling approaches can provide valuable insight to researchers across a broad range of fields. At the same time, the interdisciplinary applicability of these approaches means that both new learners and experienced researchers may not have a clear understanding of the theoretical and computational fundamentals underpinning these methods. Theoretical Foundations of Multiscale Modelling aims to address this knowledge gap, providing a clear insight to the thermodynamical and statistical mechanical foundations of methods commonly employed, and highlighting the potential and limitations of each approach.
Starting with an overview of the basics of statistical mechanics, the first chapters of the book illustrate the general theory of molecular modelling, the concepts and methods for the study of complex molecular systems, then progressing to cover key strategies employed to perform computational calculations and simulations. The second part of the book reviews how the most recent, cutting-edge tools of the trade are used in practice through a selection of case studies highlighting multiscale modelling, multiple resolution simulation methods, and machine-learning applications. This is also achieved by presenting case studies from recent scientific literature, highlighting the advantages of a multiscale treatment for the understanding of complex molecular phenomena.
Drawing on the experience of its authors, Theoretical Foundations of Multiscale Modelling is an insightful guide for all those learning, applying, or interested in exploring multiscale modelling methods for their work.
1. Chapter 1: Introduction to soft matter - p. 1 2. Chapter 2: Foundations of classical mechanics - p. 19 3. Chapter 3: Foundations of statistical mechanics - p. 39 4. Chapter 4: Structure and thermodynamics of fluids from spatial distribution functions - p. 71 5. Chapter 5: Modelling atomic systems - p. 89 6. Chapter 6: Simulation and sampling - p. 109 7. Chapter 7: Stochastic dynamics - p. 139 8. Chapter 8: Free energy and enhanced sampling - p. 155 9. Chapter 9: From critical phenomena to coarse-graining - p. 189 10. Chapter 10: Coarse-graining molecular systems - p. 217 11. Chapter 11: The relative entropy method - p. 235 12. Chapter 12: Boltzmann inversion, iterative Boltzmann inversion and inverse Monte Carlo - p. 249 13. Chapter 13: Gradient-based methods for multiscale modelling - p. 265 14. Chapter 14: Hybrid multiresolution approaches - p. 291 15. Chapter 15: Concluding remarks - p. 331 16. References - p. 337 17. Index - p. 355
