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

The Journal of Chemical Physics
Evangelia KalligiannakiPetr Plecháč

Abstract

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.

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Citations

Oct 17, 2015·The Journal of Chemical Physics·Lukas Vlcek, Ariel A Chialvo
Feb 20, 2019·Physical Review. E·Nicodemo Di PasqualeMatteo Icardi
Apr 30, 2016·Soft Matter·Pep EspañolIgnacio Zúñiga
Jul 25, 2018·The Journal of Chemical Physics·Linfeng ZhangWeinan E
Nov 4, 2020·The Journal of Chemical Physics·Jurgis RuzaRafael Gómez-Bombarelli
Feb 1, 2019·Journal of Chemical Theory and Computation·Alexander J PakGregory A Voth
Dec 12, 2017·The Journal of Physical Chemistry. B·Nicholas J H DunnW G Noid

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