The geometry of generalized force matching and related information metrics in coarse-graining of molecular systems

  • Evangelia Kalligiannaki
    University of Crete 1 Department of Mathematics and Applied Mathematics, , 70013 Heraklion, Greece
  • Vagelis Harmandaris
    University of Crete 1 Department of Mathematics and Applied Mathematics, , 70013 Heraklion, Greece
  • Markos A. Katsoulakis
    University of Massachusetts 3 Department of Mathematics and Statistics, , Amherst, Massachusetts 01003, USA
  • Petr Plecháč
    University of Delaware 4 Department of Mathematical Sciences, , Newark, Delaware 19716, USA

書誌事項

公開日
2015-08-24
DOI
  • 10.1063/1.4928857
公開者
AIP Publishing

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説明

<jats:p>Using the probabilistic language of conditional expectations, we reformulate the force matching method for coarse-graining of molecular systems as a projection onto spaces of coarse observables. A practical outcome of this probabilistic description is the link of the force matching method with thermodynamic integration. This connection provides a way to systematically construct a local mean force and to optimally approximate the potential of mean force through force matching. We introduce a generalized force matching condition for the local mean force in the sense that allows the approximation of the potential of mean force under both linear and non-linear coarse graining mappings (e.g., reaction coordinates, end-to-end length of chains). Furthermore, we study the equivalence of force matching with relative entropy minimization which we derive for general non-linear coarse graining maps. We present in detail the generalized force matching condition through applications to specific examples in molecular systems.</jats:p>

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