Genetic Algorithm Optimization of Force Field Parameters: Application to a Coarse-Grained Model of RNA

Author(s):  
Filip Leonarski ◽  
Fabio Trovato ◽  
Valentina Tozzini ◽  
Joanna Trylska
2017 ◽  
Author(s):  
Joseph F. Rudzinski ◽  
Tristan Bereau

Coarse-grained molecular simulation models have provided immense, often general, insight into the complex behavior of condensed-phase systems, but suffer from a lost connection to the true dynamical properties of the underlying system. In general, the physics that is built into a model shapes the free-energy landscape, restricting the attainable static and kinetic properties. In this work, we perform a detailed investigation into the property interrelationships resulting from these restrictions, for a representative system of the helix-coil transition. Inspired by high-throughput studies, we systematically vary force-field parameters and monitor their structural, kinetic, and thermodynamic properties. The focus of our investigation is a simple coarse-grained model, which accurately represents the underlying structural ensemble, i.e., effectively avoids sterically-forbidden configurations. As a result of this built-in physics, we observe a rather large restriction in the topology of the networks characterizing the simulation kinetics. When screening across force-field parameters, we find that structurally-accurate models also best reproduce the kinetics, suggesting structural-kinetic relationships for these models. Additionally, an investigation into thermodynamic properties reveals a link between the cooperativity of the transition and the network topology at a single reference temperature.


2016 ◽  
Vol 45 (10) ◽  
pp. 4370-4379 ◽  
Author(s):  
Johannes P. Dürholt ◽  
Raimondas Galvelis ◽  
Rochus Schmid

We have adapted our genetic algorithm based optimization approach, originally developed to generate force field parameters from quantum mechanic reference data, to derive a first coarse grained force field for a MOF, taking the atomistic MOF-FF as a reference.


2007 ◽  
Vol 13 (1s) ◽  
pp. 33-37
Author(s):  
V. Makarenko ◽  
◽  
G. Ruecker ◽  
R. Sommer ◽  
N. Djanibekov ◽  
...  

2021 ◽  
Vol 118 (23) ◽  
pp. 234001
Author(s):  
Yun Chen ◽  
Chengyuan Wang ◽  
Ya Yu ◽  
Zibin Jiang ◽  
Jinwen Wang ◽  
...  

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