Polymer Physics and Theory

This section "Polymer Physics and Theory" of Polymers aims to rapidly publish high-quality contributions that report on novel research findings regarding theoretical, computational, and experimental studies of the physico-chemical properties of polymeric systems both at equilibrium and beyond equilibrium. The focus is on the properties and features of both real and model systems. We emphasize: a) the development of multiscale modeling methodologies, constitutive equations, and new theories; b) the implementation of efficient algorithms and machine learning schemes for structure–property relationship quantification; and c) experimental studies for the preparation and characterization of new polymeric materials, including instrumentation and development of new measuring devices. While purely computational studies should help uncover or predict microscopic mechanisms underlying macroscopic measurable phenomena, theoretical studies should result in novel methods and solutions (exact or approximate), and may be supplemented by numerical investigations. The outcome of both types of contributions should ideally confront with existing experimental data, or suggest additional experiments. Purely experimental studies should help uncover or further explore novel phenomena and systems of potential relevance for applications. Investigations that aim at verifying theoretical predictions are also highly welcome.

• elasticity, mechanical properties, elastic properties, optical properties, transport properties, viscosity, diffusivity
• simulation, molecular dynamics, Brownian Dynamics, Monte Carlo, dissipative particle dynamics
• lattice models, atomistic models, coarse-graining, multiscale modelling, hierarchical modelling
• theory, field theoretic simulations, self-consistent field theory
• entanglements, topological constraints
• solutions and melts, block copolymers, mixtures, blends, brushes, ring polymers, nanocomposites, gels, networks, micelles, biological polymers, DNA, complexation, morphology, self-assembly, phase behavior
• machine learning, data mining, big data 
• nonequilibrium thermodynamics