RSC Publishing


Publishing

 

Cover image for Physical Chemistry Chemical Physics, select for current issue

Physical Chemistry Chemical Physics

High quality research in physical chemistry, chemical physics and biophysical chemistry.



Subscribers

Non-subscribers

Free access



Perspective

Phys. Chem. Chem. Phys., 2009, 11, 1869 - 1892, DOI: 10.1039/b818051b


Multiscale modeling of emergent materials: biological and soft matter

Teemu Murtola, Alex Bunker, Ilpo Vattulainen, Markus Deserno and Mikko Karttunen


In this review, we focus on four current related issues in multiscale modeling of soft and biological matter. First, we discuss how to use structural information from detailed models (or experiments) to construct coarse-grained ones in a hierarchical and systematic way. This is discussed in the context of the so-called Henderson theorem and the inverse Monte Carlo method of Lyubartsev and Laaksonen. In the second part, we take a different look at coarse graining by analyzing conformations of molecules. This is done by the application of self-organizing maps, i.e., a neural network type approach. Such an approach can be used to guide the selection of the relevant degrees of freedom. Then, we discuss technical issues related to the popular dissipative particle dynamics (DPD) method. Importantly, the potentials derived using the inverse Monte Carlo method can be used together with the DPD thermostat. In the final part we focus on solvent-free modeling which offers a different route to coarse graining by integrating out the degrees of freedom associated with solvent.

Graphical abstract image for this article  (ID: b818051b)