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.
Cascella, Michele: -
Michele Cascella got his PhD in Statistical and Biological Physics in 2004 at the International School for Advanced Studies (SISSA) in Trieste. After a postdoctoral experience at EPFL, in 2008, being awarded a Swiss National Science Foundation professorship grant, he began his independent career as Assistant Professor at the University of Bern. Since 2016, he is Full Professor in Theoretical Chemistry at the University of Oslo. His research is dedicated to the development and application of multi-scale and multi-resolution methods for biochemical systems in the condensed phase. Recent focus is on soft systems combining computer simulations with SAXS/SANS experiment, and in molecular modelling of main group organometallic compounds (Grignard reagents), using ab initio and machine learning approaches.
Potestio, Raffaello: -
Raffaello Potestio received his PhD in Statistical and Biological Physics from the International School for Advanced Studies (SISSA) in Trieste, Italy, in 2010, with a PhD thesis on coarse-grained models of protein structure and interactions. In November 2010 he started a postdoc in the Theory group at the Max Planck Institute for Polymer Research (MPIP) in Mainz, Germany; here, between August 2013 and December 2017, he was Group Leader of the Statistical Mechanics of Biomolecules group. In 2017 he was awarded an ERC starting grant and moved to the University of Trento, Italy, where he currently is associate professor in Physics. His main research interests are the development and application of coarse-graining strategies for soft and biological matter, and the investigation of the relation between model resolution and physical properties in complex systems.
