12 research outputs found
Co-non-solvency: Mean-field polymer theory does not describe polymer collapse transition in a mixture of two competing good solvents
Smart polymers are a modern class of polymeric materials that often exhibit
unpredictable behavior in mixtures of solvents. One such phenomenon is
co-non-solvency. Co-non-solvency occurs when two (perfectly) miscible and
competing good solvents, for a given polymer, are mixed together. As a result,
the same polymer collapses into a compact globule within intermediate mixing
ratios. More interestingly, polymer collapses when the solvent quality remains
good and even gets increasingly better by the addition of the better cosolvent.
This is a puzzling phenomenon that is driven by strong local concentration
fluctuations. Because of the discrete particle based nature of the
interactions, Flory-Huggins type mean field arguments become unsuitable. In
this work, we extend the analysis of the co-non-solvency effect presented
earlier [Nature Communications 5, 4882 (2014)]. We explain why co-non-solvency
is a generic phenomenon that can be understood by the thermodynamic treatment
of the competitive displacement of (co)solvent components. This competition can
result in a polymer collapse upon improvement of the solvent quality. Specific
chemical details are not required to understand these complex conformational
transitions. Therefore, a broad range of polymers are expected to exhibit
similar reentrant coil-globule-coil transitions in competing good solvents
Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes
Multiscale and inhomogeneous molecular systems are challenging topics in the
field of molecular simulation. In particular, modeling biological systems in
the context of multiscale simulations and exploring material properties are
driving a permanent development of new simulation methods and optimization
algorithms. In computational terms, those methods require parallelization
schemes that make a productive use of computational resources for each
simulation and from its genesis. Here, we introduce the heterogeneous domain
decomposition approach which is a combination of an heterogeneity sensitive
spatial domain decomposition with an \textit{a priori} rearrangement of
subdomain-walls. Within this approach, the theoretical modeling and
scaling-laws for the force computation time are proposed and studied as a
function of the number of particles and the spatial resolution ratio. We also
show the new approach capabilities, by comparing it to both static domain
decomposition algorithms and dynamic load balancing schemes. Specifically, two
representative molecular systems have been simulated and compared to the
heterogeneous domain decomposition proposed in this work. These two systems
comprise an adaptive resolution simulation of a biomolecule solvated in water
and a phase separated binary Lennard-Jones fluid.Comment: 14 pages, 12 figure
Equilibration of High Molecular-Weight Polymer Melts: A Hierarchical Strategy
A strategy is developed for generating equilibrated high molecular-weight
polymer melts described with microscopic detail by sequentially backmapping
coarse-grained (CG) configurations. The microscopic test model is generic but
retains features like hard excluded volume interactions and realistic melt
densities. The microscopic representation is mapped onto a model of soft
spheres with fluctuating size, where each sphere represents a microscopic
subchain with monomers. By varying a hierarchy of CG
representations at different resolutions is obtained. Within this hierarchy, CG
configurations equilibrated with Monte Carlo at low resolution are sequentially
fine-grained into CG melts described with higher resolution. A Molecular
Dynamics scheme is employed to slowly introduce the microscopic details into
the latter. All backmapping steps involve only local polymer relaxation thus
the computational efficiency of the scheme is independent of molecular weight,
being just proportional to system size. To demonstrate the robustness of the
approach, microscopic configurations containing up to chains with
polymerization degrees are generated and equilibration is confirmed by
monitoring key structural and conformational properties. The extension to much
longer chains or branched polymers is straightforward
One size fits all: equilibrating chemically different polymer liquids through universal long-wavelength description
Mesoscale behavior of polymers is frequently described by universal laws.
This physical property motivates us to propose a new modeling concept, grouping
polymers into classes with a common long-wavelength representation. In the same
class samples of different materials can be generated from this representation,
encoded in a single library system. We focus on homopolymer melts, grouped
according to the invariant degree of polymerization. They are described with a
bead-spring model, varying chain stiffness and density to mimic chemical
diversity. In a renormalization group-like fashion library samples provide a
universal blob-based description, hierarchically backmapped to create
configurations of other class-members. Thus large systems with
experimentally-relevant invariant degree of polymerizations (so far accessible
only on very coarse-grained level) can be microscopically described.
Equilibration is verified comparing conformations and melt structure with
smaller scale conventional simulations
Hierarchical modeling of polystyrene melts: From soft blobs to atomistic resolution
We demonstrate that hierarchical backmapping strategies incorporating generic
blob-based models can equilibrate melts of high-molecular-weight polymers,
described with chemically specific, atomistic, models. The central idea behind
these strategies, is first to represent polymers by chains of large soft blobs
(spheres) and efficiently equilibrate the melt on mesoscopic scale. Then, the
degrees of freedom of more detailed models are reinserted step by step. The
procedure terminates when the atomistic description is reached. Reinsertions
are feasible computationally because the fine-grained melt must be
re-equilibrated only locally. To develop the method, we choose a polymer with
sufficient complexity. We consider polystyrene (PS), characterized by
stereochemistry and bulky side groups. Our backmapping strategy bridges
mesoscopic and atomistic scales by incorporating a blob-based, a moderately CG,
and a united-atom model of PS. We demonstrate that the generic blob-based model
can be parameterized to reproduce the mesoscale properties of a specific
polymer -- here PS. The moderately CG model captures stereochemistry. To
perform backmapping we improve and adjust several fine-graining techniques. We
prove equilibration of backmapped PS melts by comparing their structural and
conformational properties with reference data from smaller systems,
equilibrated with less efficient methods.Comment: 18 page
Code modernization strategies for short-range non-bonded molecular dynamics simulations
Modern HPC systems are increasingly relying on greater core counts and wider
vector registers. Thus, applications need to be adapted to fully utilize these
hardware capabilities. One class of applications that can benefit from this
increase in parallelism are molecular dynamics simulations. In this paper, we
describe our efforts at modernizing the ESPResSo++ molecular dynamics
simulation package by restructuring its particle data layout for efficient
memory accesses and applying vectorization techniques to benefit the
calculation of short-range non-bonded forces, which results in an overall three
times speedup and serves as a baseline for further optimizations. We also
implement fine-grained parallelism for multi-core CPUs through HPX, a C++
runtime system which uses lightweight threads and an asynchronous many-task
approach to maximize concurrency. Our goal is to evaluate the performance of an
HPX-based approach compared to the bulk-synchronous MPI-based implementation.
This requires the introduction of an additional layer to the domain
decomposition scheme that defines the task granularity. On spatially
inhomogeneous systems, which impose a corresponding load-imbalance in
traditional MPI-based approaches, we demonstrate that by choosing an optimal
task size, the efficient work-stealing mechanisms of HPX can overcome the
overhead of communication resulting in an overall 1.4 times speedup compared to
the baseline MPI version.Comment: 24 pages, 9 figure
ESPResSo++: A modern multiscale simulation package for soft matter systems
The redesigned Extensible Simulation Package for Research on Soft matter systems (ESPResSo++) is a free, open-source, parallelized, object-oriented simulation package designed to perform many-particle simulations, principally molecular dynamics and Monte Carlo, of condensed soft matter systems. In addition to the standard simulation methods found in well-established packages, ESPResSo++ provides the ability to perform Adaptive Resolution Scheme (AdResS) simulations which are multiscale simulations of molecular systems where the level of resolution of each molecule can change on-the-fly. With the main design objective being extensibility, the software features a highly modular C++ kernel that is coupled to a Python user interface. This makes it easy to add new algorithms, setup a simulation , perform online analysis, use complex workflows and steer a simulation. The extreme flexibility of the software allows for the study of a wide range of systems. The modular structure enables scientists to use ESPResSo++ as a research platform for their own methodological developments, which at the same time allows the software to grow and acquire the most modern methods. ESPResSo++ is targeted for a broad range of architectures and is licensed under the GNU General Public License