Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)

Multi-component polymer systems are important for the development of new materials because of their ability to phase-separate or self-assemble into nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction with a soft, coarse-grained polymer model is an established technique to investigate these soft-matter systems. Here we present an im- plementation of this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is suitable to simulate large system sizes with up to billions of particles, yet versatile enough to study properties of different kinds of molecular architectures and interactions. We achieve efficiency of the simulations commissioning accelerators like GPUs on both workstations as well as supercomputers. The implementa- tion remains flexible and maintainable because of the implementation of the scientific programming language enhanced by OpenACC pragmas for the accelerators. We present implementation details and features of the program package, investigate the scalability of our implementation SOMA, and discuss two applications, which cover system sizes that are difficult to reach with other, common particle-based simulation methods

Enumeration and simulation of lattice polymers as models for compact biological macromolecules

Polymers are the main building blocks of many biological systems, and thus polymer models are important tools for our understanding. One such biological system is the large scale organisation of chromatin. A key question here, is how during cell division the chromosomes can separate without entanglement and knotting. One proposal is that this achieved by a specific spatial organisation of the chromosomes, known as the "fractal globule". Using Monte Carlo simulations, we found that fractal globules are unstable and thus cannot represent the biological system without further ingredients. Another proposal is that topological effects cause spatial separation of the chromosomes. These topological effects can be studied using simulations of nonconcatenated ring polymers. Using a compute device called the Graphics Processing Unit, very detailed and long simulations were carried out. From these a picture emerged in which ring polymers behave much slower than was found in previous studies. A second biological system studied here is the folded state of the protein. This is modeled by the Hamiltonian walk. Here, instead of simulations, we exactly enumerated all Hamiltonian walks of the 4x4x4 cube. Interestingly, simulations show that for larger systems many more walks exist than previously estimated.Theoretical Physic

Entanglement Length Scale Separates Threading from Branching of Unknotted and Non-concatenated Ring Polymers in Melts

Current theories on the conformation and dynamics of unknotted and non-concatenated ring polymers in melt conditions describe each ring as a tree-like double-folded object. While evidence from simulations supports this picture on a single ring level, other works show pairs of rings also thread each other, a feature overlooked in the tree theories. Here we reconcile this dichotomy using Monte Carlo simulations of the ring melts with different bending rigidities. We find that rings are double-folded (more strongly for stiffer rings) on and above the entanglement length scale, while the threadings are localized on smaller scales. The different theories disagree on the details of the tree structure, i.e., the fractal dimension of the backbone of the tree. In the stiffer melts we find an indication of a self-avoiding scaling of the backbone, while more flexible chains do not exhibit such a regime. Moreover, the theories commonly neglect threadings and assign different importance to the impact of the progressive constraint release (tube dilation) on single ring relaxation due to the motion of other rings. Despite that each threading creates only a small opening in the double-folded structure, the threading loops can be numerous and their length can exceed substantially the entanglement scale. We link the threading constraints to the divergence of the relaxation time of a ring, if the tube dilation is hindered by pinning a fraction of other rings in space. Current theories do not predict such divergence and predict faster than measured diffusion of rings, pointing at the relevance of the threading constraints in unpinned systems as well. Revision of the theories with explicit threading constraints might elucidate the validity of the conjectured existence of topological glass

Computationally Connecting Organic Photovoltaic Performance to Atomistic Arrangements and Bulk Morphology

Rationally designing roll-to-roll printed organic photovoltaics requires a fundamental understanding of active layer morphologies optimized for charge separation and transport, and which ingredients can be used to self-assemble those morphologies. In this review article we discuss advances in three areas of computational modeling that provide insight into active layer morphology and the charge transport properties that result. We explain the computational bottlenecks prohibiting atomistically-detailed simulations of device-scale active layers and the coarse-graining and hardware acceleration strategies for overcoming them. We review coarse-grained simulations of organic photovoltaic active layers and show that high throughput simulations of experimentally-relevant length scales are now accessible. We describe a new Python package diffractometer that permits grazing-incidence X-ray scattering patterns of simulated active layers to be compared against experiments. We explain the accurate calculation of charge-carrier mobilities from coarse-grained active layer morphologies by using atomistic backmapping, quantum chemical calculations, and kinetic Monte Carlo simulations. We employ these simulations to show that ordering of poly(3-hexylthiophene-2,5-diyl) explains a factor of 1000 improvement in charge mobility. In concert, we present a suite of computational tools enabling large-scale electronic properties of organic photovoltaics to be studied and screened for by molecular simulations

Development of High Performance Molecular Dynamics with Application to Multimillion-Atom Biomass Simulations

An understanding of the recalcitrance of plant biomass is important for efficient economic production of biofuel. Lignins are hydrophobic, branched polymers and form a residual barrier to effective hydrolysis of lignocellulosic biomass. Understanding lignin\u27s structure, dynamics and its interaction and binding to cellulose will help with finding more efficient ways to reduce its contribution to the recalcitrance. Molecular dynamics (MD) using the GROMACS software is employed to study these properties in atomic detail. Studying complex, realistic models of pretreated plant cell walls, requires simulations significantly larger than was possible before. The most challenging part of such large simulations is the computation of the electrostatic interaction. As a solution, the reaction-field (RF) method has been shown to give accurate results for lignocellulose systems, as well as good computational efficiency on leadership class supercomputers. The particle-mesh Ewald method has been improved by implementing 2D decomposition and thread level parallelization for molecules not accurately modeled by RF. Other scaling limiting computational components, such as the load balancing and memory requirements, were identified and addressed to allow such large scale simulations for the first time. This work was done with the help of modern software engineering principles, including code-review, continuous integration, and integrated development environments. These methods were adapted to the special requirements for scientific codes. Multiple simulations of lignocellulose were performed. The simulation presented primarily, explains the temperature-dependent structure and dynamics of individual softwood lignin polymers in aqueous solution. With decreasing temperature, the lignins are found to transition from mobile, extended to glassy, compact states. The low-temperature collapse is thermodynamically driven by the increase of the translational entropy and density fluctuations of water molecules removed from the hydration shell

Rheology and Structure Formation in Complex Polymer Melts

Polymeric materials are ubiquitous in our modern lives. Their many applications in complex materials are accompanied by potentially huge benefits for technological advancement. These applications range from batteries, fuel cells, molecular sieves, tires, and microelectronic devices. The ability to self-assemble into nanostructures in combination with their viscoelastic properties make polymers attractive for this wide range of applications. I perform computer simulations gaining knowledge about their properties for applications and manufacturing, to improve the understanding of these materials. The simulation of multicomponent polymer melts poses an extreme computational challenge. The large spatial extent of defects in self-assembled structures or nonperiodic metastable phases, which are prone to finite size effects, require the study of large system sizes. Hence, I use a soft, coarse-grained polymer model reducing the degrees of freedom to gain insights into long time and length scales. Consistent implementations of these models that scale well on modern GPUs accelerated HPCs hardware enable investigations with up to billions of particles. Consequently, I can address challenges that were deemed intractable before. Firstly, I analyze metastable network phases as a function of the volume fraction, f, of diblock copolymers for polymeric battery electrolytes. One polymer block provides the mechanical stability while the other is ion conducting. The focus lies on the structure of the conducting phase. Due to the trapped metastable states, I investigate systems of extreme sizes with billions of particles circumventing finite size effects. In fact, I identify fractal structures on significant length scales inside the network phase, which influence the transport properties locally. As such, this work highlights the necessity of soft models and scaling implementations obtaining insights on engineering scales. Secondly, I will investigate the simulation of viscoelastic properties of polymeric materials with soft, coarse-grained models. It is particularly challenging to correctly capture the entangled dynamics. The noncrossability of polymer backbones introduces topological constraints on the motion of the chains. A soft, coarse-grained model does not capture this noncrossability automatically. Hence, I utilize a SLSP model to mimic the entanglements via dynamic bonds. With this model and a novel technique to average the stress auto-correlation function G(t), I perform a dynamic mechanical analysis of polymer melts and a cross-linked network. The obtained storage modulus G'(w) and loss modulus G''(w) meet the expectations for a comparison with experimental studies. A nonequilibrium study of diblock copolymers in shear flow completes this work. Shear flow is a powerful method to macroscopically order a metastable microstructure. In a symmetric diblock copolymer melt, the equilibrium microstructure is a lamellar phase. The first step determines the perpendicular orientation of the lamellae in shear flow as stable at all stresses according to the concept of the Rayleighian, R. Further, I study the transition between a grain in the unstable orientation next to a grain in the stable orientation. I identify two different transition pathways. At low applied stresses, the grain boundary of the stable grain grows into the unstable grain. At higher stresses, the unstable orientation is destabilized and forms an intermediate microemulsion-like phase with no local orientation. This intermediate phase turns subsequently into the stable orientation. Oscillatory shear at high frequencies delays the onset of this microemulsion pathway. In a collaboration with Matthias Heck and Manfred Wilhelm at KIT, these transitions have been studied in LAOS experiments as well

Learning the constitutive relation of polymeric flows with memory

We develop a learning strategy to infer the constitutive relation for the stress of polymeric flows with memory. We make no assumptions regarding the functional form of the constitutive relations, except that they should be expressible in differential form as a function of the local stress- and strain-rate tensors. In particular, we use a Gaussian Process regression to infer the constitutive relations from stress trajectories generated from small-scale (fixed strain-rate) microscopic polymer simulations. For simplicity, a Hookean dumbbell representation is used as a microscopic model, but the method itself can be generalized to incorporate more realistic descriptions. The learned constitutive relation is then used to perform macroscopic flow simulations, allowing us to update the stress distribution in the fluid in a manner that accounts for the microscopic polymer dynamics. The results using the learned constitutive relation are in excellent agreement with full Multi-Scale Simulations, which directly couple micro/macro degrees of freedom, as well as the exact analytical solution given by the Maxwell constitutive relation. We are able to fully capture the history dependence of the flow, as well as the elastic effects in the fluid. We expect the proposed learning/simulation approach to be used not only to study the dynamics of entangled polymer flows, but also for the complex dynamics of other Soft Matter systems, which possess a similar hierarchy of length- and time-scales.Comment: 19 pages, 9 figure

Advances in Molecular Simulation

Molecular simulations are commonly used in physics, chemistry, biology, material science, engineering, and even medicine. This book provides a wide range of molecular simulation methods and their applications in various fields. It reflects the power of molecular simulation as an effective research tool. We hope that the presented results can provide an impetus for further fruitful studies