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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

A new configurational bias scheme for sampling supramolecular structures.

Robin De Gernier¹, Tine Curk², Galina V Dubacheva³

¹Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, Code Postal 231, Campus Plaine, B-1050 Brussels, Belgium.

The Journal of Chemical Physics

|January 3, 2015

Summary

We developed a new simulation method for reconfigurable polymer networks. This approach efficiently models complex supramolecular structures, aiding in the design of advanced materials.

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Area of Science:

Computational chemistry and materials science
Polymer physics
Supramolecular chemistry

Background:

Simulating reconfigurable supramolecular structures is computationally challenging.
Existing methods struggle with efficient sampling of dynamic polymer networks.
Understanding the behavior of functionalized polymers is crucial for materials design.

Purpose of the Study:

To introduce a novel simulation scheme for efficient sampling of reconfigurable supramolecular structures.
To enable the study of polymer networks with reactive binding sites.
To provide a versatile tool for investigating complex self-assembling systems.

Main Methods:

Utilized a configurational bias scheme combined with a non-local Monte Carlo algorithm to change network topology.
Employed multi-scale modeling, merging coarse-grained polymer simulations with experimental free energy data.
Applied the method to DNA-coated colloids and receptor-functionalized polymers on surfaces.

Main Results:

Successfully computed hybridization free energy for DNA-coated colloids.
Determined the density of states for adsorbed polymers based on ligand-receptor complex formation.
Validated results against mean-field theory predictions.

Conclusions:

The new simulation scheme efficiently samples reconfigurable supramolecular structures.
The method is versatile, applicable to various functionalized polymer systems.
This tool will advance the investigation of self-assembling polymer materials.