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Title: Rigorous force field optimization principles based on statistical distance minimization

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. We exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of the approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.
Authors:
 [1] ;  [2] ;  [1]
  1. Chemical Sciences Division, Geochemistry & Interfacial Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6110 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
22489697
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 143; Journal Issue: 14; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; ACCURACY; COMPARATIVE EVALUATIONS; ENTROPY; EXPERIMENTAL DATA; MINIMIZATION