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Related papers: Topological Analysis of Linear Polymer Melts

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In many areas of applied geometric/numeric computational mathematics, including geo-mapping, computer vision, computer graphics, finite element analysis, medical imaging, geometric design, and solid modeling, one has to compute incidences,…

Computational Geometry · Computer Science 2019-11-20 Alberto Paoluzzi , Vadim Shapiro , Antonio DiCarlo , Francesco Furiani , Giulio Martella , Giorgio Scorzelli

Polymers consisting of more than one type of monomer, known as copolymers, are vital to both living and synthetic systems. Copolymerisation has been studied theoretically in a number of contexts, often by considering a Markov process in…

In this paper we discuss two different existing algorithms for computing topological entropy and we perform one of them in order to compute the isentropes for cubic polynomials.

Dynamical Systems · Mathematics 2020-06-25 Noah Cockram , Ana Rodrigues

We investigate the chemical dissolution of porous media using a network model in which the system is represented as a series of interconnected pipes with the diameter of each segment increasing in proportion to the local reactant…

Fluid Dynamics · Physics 2015-06-12 Agnieszka Budek , Piotr Szymczak

Hybrid molecular dynamics/Monte Carlo simulations used to study melts of unentangled, thermoreversibly associating supramolecular polymers. In this first of a series of papers, we describe and validate a model that is effective in…

Soft Condensed Matter · Physics 2015-05-13 Robert S. Hoy , Glenn H. Fredrickson

Obtaining a rigorous and reliable method for linking computer simulations of polymer blends and composites at different length scales of interest is a highly desirable goal in soft matter physics. In this paper a multiscale modeling…

Soft Condensed Matter · Physics 2015-05-19 J. McCarty , M. G. Guenza

Topological Machine Learning (TML) is an emerging field that leverages techniques from algebraic topology to analyze complex data structures in ways that traditional machine learning methods may not capture. This tutorial provides a…

Machine Learning · Computer Science 2024-09-05 Baris Coskunuzer , Cüneyt Gürcan Akçora

For the first time a Fddd network structure is found in a poly(styrene-b-isoprene) (S-I) diblock copolymer melts by using small-angle X-ray scattering and transmission electron microscope, as predicted by Tyler et al. by using calculations…

Network systems consist of subsystems and their interconnections, and provide a powerful framework for analysis, modeling and control of complex systems. However, subsystems may have high-dimensional dynamics, and the amount and nature of…

Optimization and Control · Mathematics 2020-12-07 Xiaodong Cheng , Jacquelien M. A. Scherpen

More than two decades ago, combinatorial topology was shown to be useful for analyzing distributed fault-tolerant algorithms in shared memory systems and in message passing systems. In this work, we show that combinatorial topology can also…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-10-05 Armando Castañeda , Pierre Fraigniaud , Ami Paz , Sergio Rajsbaum , Matthieu Roy , Corentin Travers

We present a braided circuit topology framework for investigating topology and structural phase transitions in aggregates of semiflexible polymers. In the conventional approach to circuit topology, which specifically applies to single…

Soft Condensed Matter · Physics 2024-02-27 Jonas Berx , Alireza Mashaghi

We study the partitioning of cosolute particles in a thin film of a semi-flexible polymer network by a combination of coarse-grained (implicit-solvent) stochastic dynamics simulations and mean-field theory. We focus on a wide range of…

Soft Condensed Matter · Physics 2017-08-22 Won Kyu Kim , Arturo Moncho-Jorda , Rafael Roa , Matej Kanduc , Joachim Dzubiella

The long time dynamics of polymeric materials has been extensively studied in the past through various experimental techniques and computer simulations. While computer simulations typically treat generic, simplified models, experiments deal…

Soft Condensed Matter · Physics 2008-09-10 Vagelis A. Harmandaris , Kurt Kremer

We draw an analogy between droplet formation in dilute particle and polymer systems. Our arguments are based on finite-size scaling results from studies of a two-dimensional lattice gas to three-dimensional bead-spring polymers. To set the…

Statistical Mechanics · Physics 2018-03-14 Wolfhard Janke , Johannes Zierenberg

Conformation-dependent design of polymer sequences can be considered as a tool to control macromolecular self-assembly. We consider the monomer unit sequences created via the modification of polymers in a homogeneous melt in accordance with…

Soft Condensed Matter · Physics 2021-07-07 Elena N. Govorun , Ruslan M. Shupanov , Sophia A. Pavlenko , Alexei R. Khokhlov

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate…

Machine Learning · Computer Science 2022-10-13 Matteo Aldeghi , Connor W. Coley

A network can be analyzed at different topological scales, ranging from single nodes to motifs, communities, up to the complete structure. We propose a novel intermediate-level topological analysis that considers non-overlapping subgraphs…

Computational Physics · Physics 2009-11-13 Lucas Antiqueira , Luciano da Fontoura Costa

We investigate with numerical simulations the influence of topology and stiffness on macroscopic rheological properties of polymer melts consisting of unknotted, knotted or concatenated rings. While melts of flexible, knotted oligomer rings…

Soft Condensed Matter · Physics 2024-10-18 Ranajay Datta , Peter Virnau

Possible forms of obstructed atomic limits in quasi-one-dimensional systems are studied using line group symmetry. This is accomplished by revisiting the standard theory with an emphasis on its group-theoretical background, synthesizing the…

Other Condensed Matter · Physics 2024-12-30 Milan Damnjanovic , Ivanka Milosevic

We perform topological data analysis on the internal states of convolutional deep neural networks to develop an understanding of the computations that they perform. We apply this understanding to modify the computations so as to (a) speed…

Machine Learning · Computer Science 2018-11-06 Gunnar Carlsson , Rickard Brüel Gabrielsson