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Modeling of Structure Formation in Soft Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4644

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Guest Editor
Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668 Warsaw, Poland
Interests: polymer physics; soft matter; fluid physics; computational sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft materials are fascinating, ubiquitous, and relevant for a large spectrum of diverse applications at the interface between physics, chemistry, materials science, chemical engineering, and biology. Built by a number of different components (e.g., colloids, polymers and active matter, liquid crystals, granular matter, proteins), soft materials can adopt different structures with unique properties that manifest in micro-, meso-, and macroscales and can also contribute to many exciting phenomena.

While recent advances in experimental and theoretical methods have offered significant insights into the structure of soft materials and increased understanding of their properties, important challenges remain. Many of these challenges can be overcome by computer modeling, in this way leading to a higher degree of understanding of the structure formation of soft materials and the unveiling of the structure–property relation that is required for the design of materials with tailor-made properties. To this end, the range of modeling methods to study soft materials varies, covering a various relevant length and time scales. Moreover, new methodologies emerge that include state-of-the-art hybrid modeling approaches, such as coupled molecular dynamics–continuum methods, which aim at achieving the full multiscale description of the systems.

This Special Issue aims to achieve two objectives: (i) to present novel computational methodologies that go beyond the current state of the art and are suitable for soft materials, and (ii) to highlight important contributions to our understanding in the structure formation of soft materials by modelling methods.

Dr. Panagiotis Theodorakis
Guest Editor

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Keywords

  • modelling of soft materials
  • quantum/atomistic/molecular/continuum/stochastic modeling
  • Artificial Intelligence
  • multiscale computer simulation
  • structure formation and properties

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Published Papers (2 papers)

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Research

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12 pages, 4626 KiB  
Article
Off-Lattice Monte-Carlo Approach for Studying Nucleation and Evaporation Phenomena at the Molecular Scale
by Panagiotis E. Theodorakis, Yongjie Wang, Aiqiang Chen and Bin Liu
Materials 2021, 14(9), 2092; https://doi.org/10.3390/ma14092092 - 21 Apr 2021
Cited by 4 | Viewed by 2429
Abstract
Droplet nucleation and evaporation are ubiquitous in nature and many technological applications, such as phase-change cooling and boiling heat transfer. So far, the description of these phenomena at the molecular scale has posed challenges for modelling with most of the models being implemented [...] Read more.
Droplet nucleation and evaporation are ubiquitous in nature and many technological applications, such as phase-change cooling and boiling heat transfer. So far, the description of these phenomena at the molecular scale has posed challenges for modelling with most of the models being implemented on a lattice. Here, we propose an off-lattice Monte-Carlo approach combined with a grid that can be used for the investigation of droplet formation and evaporation. We provide the details of the model, its implementation as Python code, and results illustrating its dependence on various parameters. The method can be easily extended for any force-field (e.g., coarse-grained, all-atom models, and external fields, such as gravity and electric field). Thus, we anticipate that the proposed model will offer opportunities for a wide range of studies in various research areas involving droplet formation and evaporation and will also form the basis for further method developments for the molecular modelling of such phenomena. Full article
(This article belongs to the Special Issue Modeling of Structure Formation in Soft Materials)
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Review

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14 pages, 818 KiB  
Review
Single Chain Mean-Field Theory Study on Responsive Behavior of Semiflexible Polymer Brush
by Yingli Niu, Xiangyu Bu and Xinghua Zhang
Materials 2021, 14(4), 778; https://doi.org/10.3390/ma14040778 - 7 Feb 2021
Cited by 1 | Viewed by 1705
Abstract
The application of single chain mean-field theory (SCMFT) on semiflexible chain brushes is reviewed. The worm-like chain (WLC) model is the best mode of semiflexible chain that can continuously recover to the rigid rod model and Gaussian chain (GC) model in rigid and [...] Read more.
The application of single chain mean-field theory (SCMFT) on semiflexible chain brushes is reviewed. The worm-like chain (WLC) model is the best mode of semiflexible chain that can continuously recover to the rigid rod model and Gaussian chain (GC) model in rigid and flexible limits, respectively. Compared with the commonly used GC model, SCMFT is more applicable to the WLC model because the algorithmic complexity of the WLC model is much higher than that of the GC model in self-consistent field theory (SCFT). On the contrary, the algorithmic complexity of both models in SCMFT are comparable. In SCMFT, the ensemble average of quantities is obtained by sampling the conformations of a single chain or multi-chains in the external auxiliary field instead of solving the modified diffuse equation (MDE) in SCFT. The precision of this calculation is controlled by the number of bonds Nm used to discretize the chain contour length L and the number of conformations M used in the ensemble average. The latter factor can be well controlled by metropolis Monte Carlo simulation. This approach can be easily generalized to solve problems with complex boundary conditions or in high-dimensional systems, which were once nightmares when solving MDEs in SCFT. Moreover, the calculations in SCMFT mainly relate to the assemble averages of chain conformations, for which a portion of conformations can be performed parallel on different computing cores using a message-passing interface (MPI). Full article
(This article belongs to the Special Issue Modeling of Structure Formation in Soft Materials)
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