Deriving effective mesoscale potentials from atomistic simulations

By Dirk Reith, M. Pütz, Florian Müller-Plathe. Journal of Computational Chemistry, 2002

1,206 citations recorded by the source corpus.

We demonstrate how an iterative method for potential inversion from distribution functions developed for simple liquid systems can be generalized to polymer systems. It uses the differences in the potentials of mean force between the distribution functions generated from a guessed potential and the true distribution functions to improve the effective potential successively. The optimization algorithm is very powerful: convergence is reached for every trial function in few iterations. As an extensive test case we coarse‐grained an atomistic all‐atom model of polyisoprene (PI) using a 13:1 reduction of the degrees of freedom. This procedure was performed for PI solutions as well as for a PI melt. Comparisons of the obtained force fields are drawn. They prove that it is not possible to use a single force field for different concentration regimes. © 2003 Wiley Periodicals, Inc. J Comput Chem 13: 1624–1636, 2003

Subfields: Biomedical Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/d56cd74be79ea315381a654bc708939954bbfe34

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Citations in the corpus

Multiscale simulation of soft matter: from scale bridging to adaptive resolution. (2008, methodology): “An example of a typical procedure leading to the appropriate tabulated potential is the iterative Boltzmann inversion method (16, 24).”
Challenges in Multiscale Modeling of Polymer Dynamics (2013, background): “[212] further improved the convergence of the pair”
Coarse-grained models for proteins. (2005, background): “Bahar and Jernigan [44] developed a two-bead model with a numerical force-field for general dynamics parameterized on the statistical analysis of a set of experimental structures [51].”
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, background): “This procedure works best by starting out with the stiffest, least correlated degrees of freedom; thus, the typical order would be bond, angle, dihedral, and finally non-bonded interactions [98].”
Biological and synthetic membranes: What can be learned from a coarse-grained description? (2006, methodology): “In polymer physics, one often employs a numerical method—iterative Boltzmann inversion [230–232,246,247].”
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized (2010, methodology): “Although iterative application of the inverse Boltzmann formula has been described before [24,35,58,59], its theoretical justification, optimal definition of the reference state and scope remained unclear.”
Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy (2021, methodology): “…particular system using fine-grained simulations as a reference, often derived in a systematic way using, for instance, iterative Boltzmann inversion 30,31 or force matching 32,33 , and transferable, either bottom-up or top-down parameterizations, aiming to achieve a generally applicable potential.”