Macromolecular systems understood through multiscale and enhanced sampling techniques

Peter J. Ortoleva, Tom Keyes, Mark Tuckerman

Research output: Contribution to journalArticle

Abstract

Contemporary multiscale and ensemble methods that enable us to understand and model coarse-grained (CG) systems are studied. Since complex systems involve the fluctuating dynamics of many atoms, underlying a CG theory is ultimately a microscopic stochastic model. This suggests that multiscale theory should not only provide governing equations for the coarse-grained state but also a probabilistic accounting of the fine-grained states consistent with the slowly evolving coarse grained one. A traditional starting point for understanding the statistical state of an N-atom system is the Liouville equation (LE). The concepts underlying multiscaling also arise in the design of simulation algorithms. At present, it is not clear whether it is more fruitful to pursue universally applicable techniques or to focus on the development of approaches more tailored to specific classes of rare-event problems.

Original languageEnglish (US)
Pages (from-to)8335-8336
Number of pages2
JournalJournal of Physical Chemistry B
Volume116
Issue number29
DOIs
StatePublished - Jul 26 2012

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sampling
Liouville equation
Sampling
Atoms
Liouville equations
Stochastic models
complex systems
atoms
Large scale systems
simulation

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Materials Chemistry
  • Surfaces, Coatings and Films

Cite this

Macromolecular systems understood through multiscale and enhanced sampling techniques. / Ortoleva, Peter J.; Keyes, Tom; Tuckerman, Mark.

In: Journal of Physical Chemistry B, Vol. 116, No. 29, 26.07.2012, p. 8335-8336.

Research output: Contribution to journalArticle

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