Implicit-solvent coarse-grained modeling for polymer solutions via Mori-Zwanzig formalism

Soft Matter ◽  
2019 ◽  
Vol 15 (38) ◽  
pp. 7567-7582 ◽  
Author(s):  
Shu Wang ◽  
Zhen Li ◽  
Wenxiao Pan
Keyword(s):  
Polymer Solutions ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Implicit Solvent ◽  
Bottom Up ◽  
System P

We present a bottom-up coarse-graining (CG) method to establish implicit-solvent CG modeling for polymers in solution, which conserves the dynamic properties of the reference microscopic system.

A new spectral coarse-graining algorithm based on K-means clustering in complex networks

2019 ◽  
Vol 33 (01) ◽  
pp. 1850421 ◽  
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Original Network ◽  
Topological Information ◽  
Real World Applications ◽  
Large Scale Networks

Coarse-graining of complex networks is one of the important algorithms to study large-scale networks, which is committed to reducing the size of networks while preserving some topological information or dynamic properties of the original networks. Spectral coarse-graining (SCG) is one of the typical coarse-graining algorithms, which can keep the synchronization ability of the original network well. However, the calculation of SCG is large, which limits its real-world applications. And it is difficult to accurately control the scale of the coarse-grained network. In this paper, a new SCG algorithm based on K-means clustering (KCSCG) is proposed, which cannot only reduce the amount of calculation, but also accurately control the size of coarse-grained network. At the same time, KCSCG algorithm has better effect in keeping the network synchronization ability than SCG algorithm. A large number of numerical simulations and Kuramoto-model example on several typical networks verify the feasibility and effectiveness of the proposed algorithm.

scholarly journals Energy renormalization for coarse-graining polymers having different segmental structures

2019 ◽  
Vol 5 (4) ◽  
pp. eaav4683 ◽  
Author(s):  
Wenjie Xia ◽  
Nitin K. Hansoge ◽  
Wen-Sheng Xu ◽  
Frederick R. Phelan ◽  
Sinan Keten ◽  
...  
Keyword(s):  
Dynamic Properties ◽  
Interaction Strength ◽  
Configurational Entropy ◽  
Coarse Graining ◽  
Soft Materials ◽  
Polymer Materials ◽  
Coarse Grained ◽  
Art Form ◽  
Polymer Models ◽  
Energy Renormalization

Multiscale coarse-grained (CG) modeling of soft materials, such as polymers, is currently an art form because CG models normally have significantly altered dynamics and thermodynamic properties compared to their atomistic counterparts. We address this problem by exploiting concepts derived from the generalized entropy theory (GET), emphasizing the central role of configurational entropy sc in the dynamics of complex fluids. Our energy renormalization (ER) method involves varying the cohesive interaction strength in the CG models in such a way that dynamic properties related to sc are preserved. We test this ER method by applying it to coarse-graining polymer melts (i.e., polybutadiene, polystyrene, and polycarbonate), representing polymer materials having a relatively low, intermediate, and high degree of glass “fragility”. We find that the ER method allows the dynamics of the atomistic polymer models to be faithfully described to a good approximation by CG models over a wide temperature range.

Extraction algorithm for optimal coarse-grained networks on complex networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950081
Author(s):  
Lang Zeng ◽  
Zhen Jia ◽  
Yingying Wang
Keyword(s):  
Complex Networks ◽  
Network Science ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Kuramoto Model ◽  
Coarse Grained ◽  
Urgent Problem ◽  
Original Network ◽  
Topological Information ◽  
Extraction Algorithm

Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

Soft Matter ◽  
2020 ◽  
Vol 16 (36) ◽  
pp. 8330-8344
Author(s):  
Shu Wang ◽  
Zhan Ma ◽  
Wenxiao Pan
Keyword(s):  
Structural Properties ◽  
Dynamic Properties ◽  
Data Driven ◽  
Coarse Grained ◽  
System P

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Soft Matter ◽  
2021 ◽  
Author(s):  
Zhan Ma ◽  
Shu Wang ◽  
Minhee Kim ◽  
Kaibo Liu ◽  
Chun-Long Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Dynamic Properties ◽  
Coarse Graining ◽  
Polymer Dynamics ◽  
Coarse Grained ◽  
Atomistic Systems

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on...

scholarly journals Bottom-Up Coarse-Grained Modeling of DNA

2021 ◽  
Vol 8 ◽  
Author(s):  
Tiedong Sun ◽  
Vishal Minhas ◽  
Nikolay Korolev ◽  
Alexander Mirzoev ◽  
Alexander P. Lyubartsev ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Deoxyribonucleic Acid ◽  
Persistence Length ◽  
Coarse Graining ◽  
Potential Of Mean Force ◽  
Coarse Grained ◽  
Bottom Up ◽  
Slow Dynamic ◽  
Molecular Systems ◽  
Fine Grained

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, “melting,” and DNA condensation.

scholarly journals Assessing the transferability of common top-down and bottom-up coarse-grained molecular models for molecular mixtures

2019 ◽  
Vol 21 (4) ◽  
pp. 1912-1927 ◽  
Author(s):  
Thomas D. Potter ◽  
Jos Tasche ◽  
Mark R. Wilson
Keyword(s):  
Coarse Graining ◽  
Coarse Grained ◽  
Molecular Models ◽  
Top Down ◽  
Bottom Up ◽  
Molecular Mixtures

Assessing the performance of top-down and bottom-up coarse-graining approaches.

scholarly journals Correction: Implicit-solvent coarse-grained modeling for polymer solutions via Mori–Zwanzig formalism

Soft Matter ◽  
2019 ◽  
Vol 15 (38) ◽  
pp. 7733-7733 ◽  
Author(s):  
Shu Wang ◽  
Zhen Li ◽  
Wenxiao Pan
Keyword(s):  
Soft Matter ◽  
Polymer Solutions ◽  
Coarse Grained ◽  
Implicit Solvent

Correction for ‘Implicit-solvent coarse-grained modeling for polymer solutions via Mori–Zwanzig formalism’ by Shu Wang et al., Soft Matter, 2019, DOI: 10.1039/c9sm01211g.

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