Common computational methods for describing laminar flow of dilute polymer solutions (LFDPS) in computational physical chemistry and engineering, such as continuum fluid dynamics approaches for the solvent description in conjunction with coarse-grained modeling for the solvated polymers, rely on sets of user-provided parameters poorly amenable to reproduce specific molecular characteristics at the atomic scale of the addressed system. In recent years, a flow molecular dynamics methodology has been shown to be a viable approach for simulating flows of molecular solutions. However, cases developed so far for condensed phase modeling based on this approach have been highly scarce. Here, we investigate the suitability of a de novo nonequilibrium molecular dynamics NEMD as adapted through our custom modified OPLS-AA force field and applied to LFDPS considering three solvents of different viscosities, water, a 50:50 water/glycerol mixture, and glycerol, and two thermoresponsive polymer derivatives of polyacrylamide, PNIPAM and PDEA. We show that the strengths of both computational approaches yield a descriptive atomistic perspective of the directed flow applied to dilute low molecular weight (LMW) polymer solutions in all of the three solvents considered, evidencing along 200 ns the spatiotemporal mechanism of energy and polymer structure changes that an applied flow triggers for elongating a globular polymer without modifying the laminar behavior of the flowing solution. We additionally demonstrate that the mechanism for the polymer structure change from globular to extended coil requires that the applied flow velocity should be at or above a threshold value v th for polymer elongation to occur, thus evidencing a novel simulation parameter. By employing two LMW polymers that are thermoresponsive, easy to synthesize, and commercially reachable, we further demonstrate with pioneering in silico experiments that the LFDPS systems are realizable at temperatures 10-40 K higher than standard thermodynamic conditions. Hence, in silico LFDPS experiments with thermoresponsive polymers have two physics-based parameters, the flow velocity of the solution and the temperature variations around the lower critical solution temperature, making them a desirable selection for several applications including microfluidics for analyses and biosensing where the polymer's ability to stretch and contract is fundamental.

Polymers exhibiting thermoresponsive behavior above a lower critical solution temperature (LCST) undergo a coil-to-globule phase transition that has many biomedical applications, including biosensing, the control of release devices, and gene or drug delivery systems. In addition, there has been sustained scientific interest in these polymers for their use in industrial applications, including water treatment and desalination. Since the coil-to-globule phase transition is greatly affected by the hydrophilic/hydrophobic balance of the polymer-solvent interactions, the LCST of a particular thermoresponsive polymer depends on the solvent environment and can be tuned through the modification of solution parameters such as co-solvent molar concentrations. While there have been numerous experimental and computational studies focused on the properties of these polymers in aqueous solutions, study of their behavior in more viscous solvents has been limited. In this article, the thermoresponsive behavior of poly (N-isopropylacrylamide) (PNIPAM) and poly (N,N-diethylacrylamide) (PDEA) has been evaluated when in solution with water, the highly viscous liquid glycerol, and both 50:50 and 90:10 glycerol:water mixtures. The adopted methodology includes molecular dynamics techniques and a modified OPLS all-atom force field, which is particularly challenging when the monomers of the targeted polymers have side-chains consisting of a hydrophobic isopropyl group and a hydrophilic amide group along the carbon backbone chain. Hence, our approach entailed simulations at the microsecond scale. The structural and energetic properties of the polymers were characterized, including radius of gyration, solvent accessible surface area, polymer-solvent hydrogen bonding, and interaction energies. Our predictions indicate that these polymers sustain a coil-to-globule phase transition in glycerol solvents at significantly higher LCSTs when compared to the LCST in less viscous aqueous solutions. These predictions highlight valuable insights that will prove advantageous for industrial and nano-scale applications requiring polymer phase behavior with elevated LCST well above ambient temperature.

A novel model potential is developed for simulating oxidised oligopyrroles in condensed phases. The force field is a coarse grained model that represents the pyrrole monomers as planar rigid bodies with fixed charge and dipole moment and the chlorine dopants as point atomic charges. The analytic function contains 17 adjustable parameters that are initially fitted on a database of small structures calculated within all-electron density functional theory. A subsequent potential function refinement is pursued with a battery of condensed phase isothermal-isobaric Metropolis Monte Carlo in-silico simulations at ambient conditions with the goal of implementing a hybrid parametrization protocol enabling agreement with experimentally known thermodynamic properties of oxidised polypyrrole. The condensed system is composed of oligomers containing 12 monomers with a 1:3 dopant-to-monomer concentration. The final set of force field optimised parameters yields an equilibrium density of the condensed system at ambient conditions in excellent agreement with oxidised polypyrrole samples synthesised in wet-laboratories.

Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an in silico protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed in silico protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.

The interface between solid poly(lactic acid-co-glycolic acid), PLGA, and solvents is described by large-scale atomistic simulations for water, ethyl acetate, and the mixture of them at ambient conditions. Interactions at the interface are dominated by Coulomb forces for water and become overwhelmingly dispersive for the other two solvents. This effect drives a neat liquid-phase separation of the mixed solvent, with ethyl acetate covering the PLGA surface and water being segregated away from it. We explore with all-atom Molecular Dynamics the formation of macromolecular assemblies on the surface of the PLGA-solvent interface when DSPE-PEG, 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-(polyethylene glycol)n amine, is added to the solvent. By following in time the deposition of the DSPE-PEG macromolecules onto the PLGA surface, the mechanism of how nanopatches remain adsorbed to the surface despite the presence of the solvent is probed. These patches have a droplet-like aspect when formed at the PLGA-water interface that flatten in the PLGA-ethyl acetate interface case. Dispersive forces are dominant for the nanopatch adhesion to the surface, while electrostatic forces are dominant for keeping the solvent around the new formations. Considering the droplet-like patches as wetting the PLGA surface, we predict an effective wetting behavior at the water interface that fades significantly at the ethyl acetate interface. The predicted mechanism of PEG-lipid nanopatch formation may be generally applicable for tailoring the synthesis of asymmetric PLGA nanoparticles for specific drug delivery conditions.

Conductive polymers are organic conjugated polymer chains with semiconducting ability that display unique mechanical properties without being thermoformable. Here we present a novel coarse-grained force field for modeling the oxidized phase of polypyrrole containing electronegative atomic dopants. The polypyrrole oligomers in this study have 12 monomers in length with a doping concentration of 25%. The polymer properties are determined using the isothermal-isobaric adaptive tempering Monte Carlo and the Metropolis Monte Carlo with codes optimized for GPUs. Several thermodynamic and mechanical properties are calculated along the caloric curve. When comparing with experiments, densities and bulk moduli perform very well yielding values in the range of 1.20–1.22 g/cm3 and 67–120 MPa, respectively. Comparing with our published model potential for the neutral polypyrrole phase, the oxidized phase presents about 30% increase in density, which is also in agreement with experiments. The computational implementation is easily portable for the inspection of other polymeric materials.

Soft matter materials and polymers are widely used in the controlled delivery of drugs. Simulation and modeling provide insight at the atomic scale enabling a level of control unavailable to experiments. We present a workflow protocol for modeling, simulating, and analyzing structural and thermodynamic response properties of poly(lactic-co-glycolic acid) (PLGA), a well-studied and FDA-approved material. We concatenate a battery of molecular dynamics, computational chemistry, highly parallel scripting, and analysis tools for generating properties of bulk polymers in the condensed phase. We provide the workflow leading to the glass transition temperature, enthalpy, density, isobaric heat capacity, thermal expansion coefficient, isothermal compressibility, bulk modulus, sonic velocity, cohesive energy, and solubility parameters. Calculated properties agree very well with experiments, when available. This methodology is currently being extended to a variety of polymer types and environments.

Extensive all-atom molecular dynamics studies of polyethylene glycol (PEG(2000)) when solvated and in the polymer bulk condensed phases were performed across a wide temperature range. We proposed two modified all-atom force field and observed the fate of the PEG(2000) macromolecule when solvated in water, water with 4% ethanol, and ethyl acetate. In aqueous solutions, the macromolecule collapsed into a prolate spheroidal ball-like structure while adopting a rather elongated coiled structure in ethyl acetate. Inspection of the polymer-condensed phases across the 150-340 K temperature range enabled the atomistic view of the solid glass below the glass transition temperature of 230 K < T-g < 250 K and the rubber behavior above T-g. Predicted properties include the enthalpy, density, and cohesive energy temperature behavior, the specific heat, thermal expansivity, thermal compressibility, bulk modulus, and Hildebrand solubility parameter both below and above T-g. Within the polymer matrix, the PEG(2000) macromolecules were entangled displaying a wide distribution of sizes that persisted when transitioning from the glass to the rubbery phases. Calculated properties agree very well with experiments when available or stand as crucial predictions while awaiting experimental measurement. Understanding the thermodynamics and structure of this useful polymer enables the efficient prediction of its behavior when building novel composite materials for nanomedicine and nanotherapeutics.

All-atom molecular dynamics is used to investigate the structural, energetic, and dynamical properties of polyacrylamide (PAM) oligomers of different lengths solvated in pure glycerol, a 90:10 glycerol–water mixture, and pure water. We predict that the oligomers’ globular structure is obtained only when the modeling strategy considers the solvent as a continuous background. Meanwhile, for all-atom modeled solvents, the glycerol solutions display a strong tendency of trapping the oligomers in instantaneous elongated random coiled structures that remain locked-in over tens of nanoseconds. In pure water, the oligomers acquire considerably shorter random coiled structures of increased flexibility. The all-atom force field, generalized amber force field, is modified by including restrained electrostatic potential atomic charges for both glycerol and PAM. Three PAM oligomer lengths containing 10, 20, and 30 monomers are considered in detail by monitoring the radius of gyration, end-to-end distance, intra-potential energy, and solvent–oligomer interaction energies for decades of nanoseconds. The density and radial distribution function of glycerol solutions are calculated when modeled with the modified atomic charges, showing a very good agreement with the experimental results at temperatures around 300 K. Glycerol has multiple applications, including its use in gel formation for PAM gel electrophoresis. Our findings are relevant for the design of sensors based on microfluidics and tailored pharmaceutical buffer solutions.

Poly-lactic-co-glycolic acid (PLGA) is a biodegradable co-polymer with common use in nanoparticle drug encapsulation. Although well studied experimentally, the mechanical behavior of PLGA is not well understood at the atomic level. Here, we develop atomic charges for the all-atom Generalized Amber Force Field (GAFF) and conduct all-atom molecular dynamics simulations of PLGA with a 50:50 ratio between its two constituent monomers for five samples of the polymer condensed phases that span 1579 u to 20183 u in molecular weight. We predict several PLGA properties that will improve the knowledge of its atomistic organization in the glassy solid, rubber, and liquid states. We report the impact of molecular weight on cohesive energy, solubility, thermodynamic response properties, structural properties related to chain entanglement, and glass transition temperatures. Properties are compared against known experimental values when available. We find that the restrained electrostatic potential atomic charges are better for simulating the caloric curve leading to the glass transition temperature, which agrees very well with experiments.

Abstract It is well known that the mixing of two or more species in flows at low Reynolds numbers cannot be easily achieved since inertial effects are essentially absent and molecular diffusion is slow. To achieve mixing in Newtonian fluids under these circumstances requires innovative new ideas such as the use of external body forces (eg, electromagnetic mixers) or the stretching and folding of fluid elements (eg, chaotic advection). For non‐Newtonian fluids with elasticity, mixing can be achieved by enabling the emergence of elastic instabilities that results in chaotic flows in which mixing is significantly enhanced. In this work, our goal is to demonstrate that clearly identifiable vortical structures (eg, vortex rings) can be generated in a viscoelastic fluid initially at rest by the release of elastic stresses. In turn, these vortex motions promote bulk mixing by transporting fluid elements from one location to another more efficiently than diffusion alone. We demonstrate this first theoretically by using the finitely extensible nonlinear elastic Peterlin (FENE‐P) model to show that elastic forces can generate torque. Using this model, we derive an expression for the time rate of change of vorticity in an elastic fluid initially at rest caused by a sudden release of stored elastic stress. This process can be thought of as the release of elastic energy from a stretched rubber band that is suddenly cut at its center. We confirm this ansatz by performing a series of direct numerical simulations based on an in‐house pseudo‐spectral code that couples the FENE‐P model to the equations of motion for an incompressible fluid. The simulations reveal that a pair of vortex rings traveling in opposite directions, with Reynolds numbers on the order of one, is generated from the sudden release of elastic stresses. Secondary vortical structures are also generated. In the concluding section of this work, we address the potential for vortex motions generated by elastic stresses to promote mixing in microflows, and we describe a possible experiment that may demonstrate this effect.

Combining atomistic simulations and machine learning techniques can expedite significantly the materials discovery process. We present an application of such methodological combination for the prediction of the melting transition and amorphous-solid behavior of the NaK alloy at the eutectic concentration. We show that efficient prediction of these properties is possible via machine learning methods trained on the topological local structural properties. The configurations resulting from Monte Carlo annealing of the NaK eutectic alloy are analyzed with topological attributes based on the Voronoi tessellation and using expectation-maximization clustering and Random Forest classification. We show that the Voronoi topological fingerprints make an accurate and fast prediction of the alloy thermal behavior by cataloguing the atomic configurations into three distinct phases: liquid, amorphous solid, and crystalline solid. Melting is found at 230 K by the sharp split of configurations classified as crystalline solid and as liquid. With the proposed metrics, an arrest-motion temperature is identified at 130–140 K through a top down clustering of the atomic configurations catalogued as amorphous solid. This statistical learning paradigm is not restricted to eutectic alloys or thermodynamics, extends the utility of topological attributes in a significant way, and harnesses the discovery of new material properties.

A tertiary structure governs, to a great extent, the biological activity of a protein in the living cell and is consequently a central focus of numerous studies aiming to shed light on cellular processes central to human health. Here, we aim to elucidate the structure of the Rift Valley fever virus (RVFV) L protein using a combination of in silico techniques. Due to its large size and multiple domains, elucidation of the tertiary structure of the L protein has so far challenged both dry and wet laboratories. In this work, we leverage complementary perspectives and tools from the computational-molecular-biology and bioinformatics domains for constructing, refining, and evaluating several atomistic structural models of the L protein that are physically realistic. All computed models have very flexible termini of about 200 amino acids each, and a high proportion of helical regions. Properties such as potential energy, radius of gyration, hydrodynamics radius, flexibility coefficient, and solvent-accessible surface are reported. Structural characterization of the L protein enables our laboratories to better understand viral replication and transcription via further studies of L protein-mediated protein–protein interactions. While results presented a focus on the RVFV L protein, the following workflow is a more general modeling protocol for discovering the tertiary structure of multidomain proteins consisting of thousands of amino acids.

This study focuses on the solvent effects that promote preferred solvated structures of polylactic-co-glycolic acid (PLGA) oligomers of molecular weight 278, 668, and 1449 u in ethyl acetate, water, and a mixture of both solvents. Our methodology consists of all-atom, explicit solvent molecular dynamics simulations for inspection of the solvated oligomer structures at ambient conditions. Parameters for the generalized Amber force field are developed in this work for the ethyl acetate liquid and the PLGA oligomers. Energetics, oligomer radius of gyration, end-to-end distance, orientational order parameter, flexibility coefficient, and backbone dihedral angles are reported along with a size scaling property yielding a power law for PLGA oligomers in each of the three solvents considered. It is found that the PLGA oligomer has two characteristic states identified by a set of extended structures and a set of collapsed structures, the former being energetically preferred in ethyl acetate and its mixture with water. The two types of PLGA structures occur in the three solvents and although they flip from one to the other in a sporadic fashion, in ethyl acetate, the extended structures may persist for more than 20 ns. The collapsed structures are significantly more frequent in water, occurring seldom in the mixed ethyl acetate-water solvent. PLGA is a biodegradable polymer approved for use in pharmaceutical and biomedical applications. Insights provided therein are of importance for the polymer aggregation process and its glassy state in condensed phases.

Due to the central role that tertiary structure plays in determining protein function, resolving protein tertiary structures is an integral research thrust in both wet and dry laboratories. Dry laboratories have primarily focused on small- to medium-size proteins. However, proteins central to human biology and human health are often quite complex, containing multiple domains and consisting of thou- sands of amino acids. Such proteins are challenging for various reasons, including the inability to crystallize. We present a case study of structure determination for the Rift Valley fever virus L-protein, a a large, multi-domain protein with currently no available tertiary structure. We employ this case study as an emerging paradigm and demonstrate how to leverage the rich and diverse landscape of bioinformatics tools for building tertiary structure models for multi-domain proteins with thousands of amino acids.

Polypyrrole/graphene composite films are gaining importance for designing technologically interesting materials and for exploring fundamental properties of novel nanointerfaces. However, the adsorption mechanism of the polymer on the graphene surface is not well understood. In this work, the stability, molecular structure, and electronic structure of neutral polypyrrole (PPy) chains adsorbed on graphene are investigated with density functional theory (DFT). Energetically stable structures are attained when the polymer adsorbs forming a 60(degrees) angle with the graphene surface. Three graphene adsorption sites are considered for the 60(degrees) direction: top, hollow, and bridge. The polymer adsorbs on these sites at a distance of 3.5 A(degrees) and a 23(degrees) tilt angle. The adsorption binding energy of the nanofilm is exclusively due to the van der Waals corrections of DFT. Based on these firstprinciples results, we assert that PPy physisorbs on graphene. Importantly, the physisorbed PPy chains do not affect significantly the band structure of graphene and do not open an energy band gap at the Dirac point. Adding a second layer of graphene on top of the polymer chains was also considered.

Metropolis Monte Carlo simulations of the eutectic NaK alloy are performed using the Second Moment Approximation (SMA) model potential across a wide range of temperatures at constant pressure. The alloy structure and thermodynamics are analyzed along with the atomic level structures using a variety of structure identification methods. Both enthalpy and density are followed along an annealing process that reveals a clear melting point around 260 K. At lower temperatures, two thermodynamic branches are identified as crystalline and amorphous solids.

A logarithmic oscillator has the outstanding property that the expectation value of its kinetic energy is constant for all stationary states. Recently the ansatz that this property can be used to define a Hamiltonian thermostat has been put forward and a suggestion has been made that this logarithmic oscillator weakly coupled to a small system would serve as a thermostat as long as few degrees of freedom are involved as is the case in atomic clusters. We have applied these ideas to a cluster of four Lennard-Jones atoms and inspected two different models of coupling between the cluster and the logarithmic oscillator in three dimensions. In both cases we show that there is a clear generation of kinetic motion of the cluster center of mass, but that kinetic energy due to interatomic vibrations is not significantly affected by coupling to the logarithmic oscillator. This is a failure of the published ansatz, as the logarithmic oscillator is unable to modify the kinetic energy due to vibrations in small atomic clusters.

We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the GPU. To fully take advantage of this mechanism, efficient GPU realizations of algorithms used to perform atomistic and molecular simulations are essential. Our system implements a versatile molecular engine, including inter-molecule interactions and orientational variables for performing the Metropolis Monte Carlo (MMC) algorithm, which is one type of Markov chain Monte Carlo. By combining memory objects with floating-point code fragments we have implemented an MMC parallel engine that entirely avoids the communication time of molecular data at runtime. Our runtime acceleration system is a forerunner of a new class of CPU–GPU algorithms exploiting memory concepts combined with threading for avoiding bus bandwidth and communication. The testbed molecular system used here is a condensed phase system of oligopyrrole chains. A benchmark shows a size scaling speedup of 60 for systems with 210,000 pyrrole monomers. Our implementation can easily be combined with MPI to connect in parallel several CPU–GPU duets.

Cloud computing is attracting the attention of the scientific community. In this paper, we develop a new cloud-based computing system in the Windows Azure platform that allows users to use the Zeolite Structure Predictor (ZSP) model through a Web browser. The ZSP is a novel machine learning approach for classifying zeolite crystals according to their framework type. The ZSP can categorize entries from the Inorganic Crystal Structure Database into 41 framework types. The novel automated system permits a user to calculate the vector of descriptors used by ZSP and to apply the model using the Random Forestalgorithm for classifying the input zeolite entries. The workflow presented here integrates executables in Fortran and Python for number crunching with packages such as Weka for data analytics and Jmol forWeb-based atomistic visualization in an interactive compute system accessed through the Web. The compute system is robust and easy to use. Communities of scientists, engineers, and students knowledgeable in Windows-based computing should find this new workflow attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous components. Copyright (c) 2012 John Wiley & Sons, Ltd.