Iterative Boltzmann inversion methods

Both the inverse Monte Carlo and iterative Boltzmann inversion methods are semi-automatic since the radial distribution function needs to be re-evaluated at... 

RDFs calculated from a CG simulation using these initial interaction potentials differ from those calculated from the atomistic simulation. An inverse Monte Carlo algorithm is therefore used to iteratively refine these interaction potentials by correcting them by the difference between the CG and atomistic RDFs. This is essentially the same method that was used by Shelley et al to derive the parameters between the CG lipid head group particles, and is also similar to the Boltzmann inversion method, " which also uses an iterative procedure that uses RDFs measured from atomistic simulations to derive CG interaction potentials. Lyubartsev has used this method to fully parameterize his own CG model of DMPC. 

Concerning the secrnid option to generate numerically a tabulated potential that closely reproduces a given melt structure, the iterative Boltzmann inversion (IBl) method [29,41, 51, 52] has been developed. 

Iterative Boltzmann Inversion Coarse Graining (IBICG). IBICG method was introduced and successfully applied to macromolecules by Kremer et and Muller-Plathe et jjjg method consists of steps like... 

In structure matching methods, potentials between the CG sites are determined by fitting structural properties, typically radial distribution functions (RDF), obtained from MD employing the CG potential (CG-MD), to those of the original atomistic system. This is often achieved by either of two closely related methods, Inverse Monte Carlo [12-15] and Boltzmann Inversion [5, 16-22], Both of these methods refine the CG potentials iteratively such that the RDF obtained from the CG-MD approaches the corresponding RDF from an atomistic MD simulation. 

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