Theoretical Foundations of Multiscale Modelling aims to address thermodynamical and statistical mechanical foundations of methods commonly employed, highlighting the potential and limitations of each approach. Starting with an overview of the basics of statistical mechanics, the book's chapters illustrate the general theory of molecular modelling, the concepts and methods for the study of complex molecular systems, key strategies employed to perform computational calculations and simulations, recent, cutting-edge tools of the trade, and a selection of case studies that highlight multi-scale modeling, multiple-resolution simulation methods, and machine-learning applications.

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.

Contenu

Introduction to soft matter

PART I - Statistical mechanics and simulation2. Foundations of Classical mechanics3. Foundations of Statistical Mechanics4. Modelling atomic systems5. Simulation and sampling6. Stochastic dynamics7. Free energy calculations

PART II - Coarse-graining8. From critical phenomena to coarse-graining9. Coarse-graining molecular systems10. Structure and thermodynamics from spatial distribution functions11. The relative entropy method12. Boltzmann Inversion, Iterative Boltzmann Inversion and Inverse Monte Carlo13. Multi-scale coarse-graining and force matching

PART III - Case studies 14. State of the art approaches to multi-scaling 15. Applications: Simple liquids16. Applications: Macromolecules

PART IV - conclusions and perspectives17. Conclusions and perspectives