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Monte Carlo simulations in the grand canonical ensemble were used to obtain critical parameters and conditions leading to microphase separation for uncharged block copolymers with solvophilic and solvophobic segments. Solvent selectivity was systematically varied to distinguish between systems that undergo direct macrophase separation and ones that initially microphase separate in the dilute phase. Finite-size scaling was used to obtain the critical parameters. Interestingly, corrections to scaling increase significantly for systems that form aggregates. The threshold value of solvent selectivity for aggregation was determined for symmetric diblock chains of varying length. The results suggest that long diblock copolymers form micelles in the dilute phase prior to macrophase separation, even in marginally selective solvents. The dependence of critical temperature on solvent selectivity was also obtained for triblock, multiblock, and alternating chains. For highly selective solvents, strong structuring in both dilute and dense phases makes it harder to reach equilibrium.

The design of functional polymeric materials with tunable response requires a synergetic use of macromolecular architecture and interactions. Here, we combine experiments with computer simulations to demonstrate how physical properties of gels can be tailored at the molecular level, using star block copolymers with alternating block sequences as a paradigm. Telechelic star polymers containing attractive outer blocks self-assemble into soft patchy nanoparticles, whereas their mirror-image inverted architecture with inner attractive blocks yields micelles. In concentrated solutions, bridged and interpenetrated hexagonally packed nanocylinders are formed, respectively, with distinct structural and rheological properties. The phase diagrams exhibit a peculiar re-entrance where the hexagonal phase melts upon both heating and cooling because of solvent–block and block–block interactions. The bridged nanostructure is characterized by similar deformability, extended structural coherence, enhanced elasticity, and yield stress compared to micelles or typical colloidal gels, which make them promising and versatile materials for diverse applications.

We developed a first-principles machine learning model for the reactive vapor–liquid phase behavior of molten Li2CO3. The model was trained on ab initioelectronic density functional theory data using the Deep Potential (DP) methodology, and its accuracy was evaluated by comparing model predictions of density and viscosity to experimental measurements. Direct coexistence simulations with the DP model over time scales of tens of nanoseconds were used to observe equilibrium dissociation of Li2CO3into CO2residing primarily in the vapor phase and Li2O which remains dissolved in the liquid. The simulations covered a range of temperatures, overall system sizes, and vapor-to-liquid volume ratios. Results were analyzed in terms of the observed chemical composition of the liquid and vapor phases, product structure, and CO2partial pressures. In addition, we calculated equilibrium constants for the dissociation reaction by assuming ideal-solution behavior for the liquid. As expected on the basis of thermodynamic arguments and prior experiments for this system, the observed partial pressure of CO2in the gas phase depends on both the temperature and the ratio of vapor to liquid volumes, while the calculated equilibrium constants only depend on temperature. DP model predictions for the equilibrium constant of the reaction are generally consistent with the available experimental measurements. The present study establishes the validity of the DP methodology for the description of reactive, multiphase equilibria from first principles, with possible applications to many other systems of scientific and technological interest even in the absence of relevant experimental measurements.

Endogenous biomolecular condensates, composed of a multitude of proteins and RNAs, can organize into multiphasic structures with compositionally distinct phases. This multiphasic organization is generally understood to be critical for facilitating their proper biological function. However, the biophysical principles driving multiphase formation are not completely understood. Here we use in vivo condensate reconstitution experiments and coarse-grained molecular simulations to investigate how oligomerization and sequence interactions modulate multiphase organization in biomolecular condensates. We demonstrate that increasing the oligomerization state of an intrinsically disordered protein results in enhanced immiscibility and multiphase formation. Interestingly, we find that oligomerization tunes the miscibility of intrinsically disordered proteins in an asymmetric manner, with the effect being more pronounced when the intrinsically disordered protein, exhibiting stronger homotypic interactions, is oligomerized. Our findings suggest that oligomerization is a flexible biophysical mechanism that cells can exploit to tune the internal organization of biomolecular condensates and their associated biological functions.

We developed a deep potential machine learning model for simulations of chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using an active learning procedure. We tested the deep neural network (DNN) potential and training procedure against reaction kinetics, chemical composition, and diffusion coefficients obtained from density functional theory (DFT) molecular dynamics calculations. The DNN potential was found to match DFT results for the structural, transport, and short-time chemical reactions in the melt. Using the DNN potential, we extended the time scales of observation to 2 ns in systems containing thousands of atoms, while preserving quantum chemical accuracy. This allowed us to reach chemical equilibrium with respect to several chemical species in the melt. The approach can be generalized for a broad spectrum of chemically reactive systems.

This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct ab initiosimulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.

We obtain activity coefficients and solubilities of NaCl in water–methanol solutions at 298.15 K and 1 bar from molecular dynamics (MD) simulations with the Joung–Cheatham, SPC/E, and OPLS-AA force fields for NaCl, water, and methanol, respectively. The Lorentz–Berthelot combining rules were adopted for the unlike-pair interactions of Na+, Cl–, and the oxygen site in SPC/E water, and geometric combining rules were utilized for the remainder of the cross interactions. We found that the selection of appropriate combining rules is important in obtaining physically realistic solubilities. The solvent compositions studied range from pure water to pure methanol. Several salt concentrations were investigated at each solvent composition, from the lowest concentrations permitted by the system size used up to the experimental solubilities. We first calculated individual ion activity coefficients (IIACs) for Na+and Cl–from the free energy change due to the gradual insertion of a single cation or anion into the solution, accompanied by a neutralizing background. We obtained the salt solubilities by comparing the chemical potentials in solution with solid NaCl chemical potentials calculated previously using the Einstein crystal method. Mean ionic activity coefficients obtained from the IIACs are in reasonable agreement with experimental data, with deviations increasing for solutions of higher methanol content. Predictions for the salt solubility are in surprisingly good agreement with experimental data, despite well-known challenges in the simultaneous calculation of activity coefficients and solubilities with classical MD simulations. The present study demonstrates that good predictions for these two important phase equilibrium properties can be obtained for mixed-solvent electrolyte solutions using existing nonpolarizable models and further suggests that the previously proposed single ion insertion technique can be extended to complex mixed-solvent solutions as well.

We compute individual ion activity coefficients (IIACs) in aqueous NaCl, KCl, NaF, and KF solutions from explicit-water molecular dynamics simulations. Free energy changes are obtained from insertion of single ions—accompanied by uniform neutralizing backgrounds—into solution by gradually turning on first Lennard-Jones interactions, followed by Coulombic interactions using Ewald electrostatics. Simulations are performed at multiple system sizes, and all results are extrapolated to the thermodynamic limit, thus eliminating any possible artifacts from the neutralizing backgrounds. Because of controversies associated with measurements of IIACs from electrochemical cells with ion-selective electrodes, the reported experimental data are not widely accepted; thus there remains a knowledge gap with respect to the contributions of individual ions to solution nonidealities. Our results are in good qualitative agreement with these reported measurements, though significantly larger in magnitude. In particular, the relative positioning for the activity coefficients of anions and cations matches the experimental ordering for all four systems. This work establishes a robust thermodynamic framework, without a need to invoke extra hypotheses, that sheds light on the behavior of individual ions and their contributions to nonidealities of aqueous electrolyte solutions.

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initioaccuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.

We performed ab initiomolecular dynamics simulations of a molten [Li0.6K0.4]3CO3OH electrolyte containing dissolved CO2and confirmed the presence of pyrocarbonate, bicarbonate, and water along with the constituent ions and molecular CO2. Our calculations indicate kinetics-driven formation of pyrocarbonate whereas bicarbonate and water are thermodynamically favored. Our results also demonstrate the presence of water at higher concentrations (double or more) than that of CO2, which reinforces the conclusions in our earlier work [AIChE J.2020, e16988] based on chemical reaction equilibrium simulations. Structural analysis indicates a larger distortion in water geometry, due to its higher polarizability compared to the nonpolar CO2, explaining the higher reactivity and smaller average lifetime of H2O in the melt. The computed lifetime distributions of the reaction products reveal that the bicarbonate ion lives the shortest among all the species present in the system. It initiates a sequence of successive proton exchange events; such sequences of exchanges along a hydrogen-bonded network gives the Grotthuss mechanism for proton transport in liquid water. The estimated proton diffusion, based on a random walk model, is about 30 times faster than the hydroxide diffusion obtained from classical molecular dynamics simulations. We believe that the presence of proton transfer events in the system has a large impact on the overall ion dynamics and electrical conductivity of the medium.

We use molecular dynamics simulations based on a recently developed force field to obtain the viscosity, ionic conductivity, and liquid–vapor surface tension of molten alkali-metal carbonate–hydroxide mixtures over a range of cation and hydroxide compositions. Recent experimental and simulation studies have suggested that molten carbonates contain non-negligible amounts of hydroxide ions in the presence of water at low partial pressures of CO2. However, due to the high temperatures (≃600 °C or higher) required to melt pure alkali carbonates and their mixtures, there is a lack of experimental thermodynamic and transport data for these molten phases. Here, we deploy a recently parametrized force field for molten alkali carbonates and hydroxides [J. Chem. Theory Comput.2020, 16, 5736−5746, DOI: 10.1021/acs.jctc.0c00285] to simulate some physical properties using atomistic molecular dynamics. Our predictions show a consistent decrease in viscosity and an increase in ionic conductivity with increasing hydroxide fractions, whereas a higher Li+mole fraction leads to an increase in both viscosity and ionic conductivity. The computed surface tension values exhibit an upward trend with higher asymmetry in cation and anion sizes. Structural analysis suggests that in the carbonate–hydroxide melts the smaller/harder ions, OH–and Li+, are more favored at the interface than the larger/softer ions, CO32–and K+/Na+. The current approach provides a systematic route to obtaining physicochemical properties of molten alkali carbonate and hydroxide mixtures over a large domain of chemical compositions and thus can steer future experimental research for molten carbonates and their applications.

Molten alkali-metal carbonates and hydroxides play important roles in the molten carbonate fuel cell and in Earth’s geochemistry. Molecular simulations allow us to study these systems at extreme conditions without the need for difficult experimentation. Using a genetic algorithm to fit ab intiomolecular dynamics-computed densities and radial distribution functions, as well as experimental enthalpies of formation, we derive new classical force fields able to accurately predict liquid chemical potentials. These fitting properties were chosen to ensure accurate liquid phase structure and energetics. Although the predicted dynamics is slow when compared to experiments, in general the trends in dynamic properties across different systems still hold true. In addition, these newly parametrized force fields can be extended to the molten carbonate–hydroxide mixtures by using standard combining rules.

We obtain the activity coefficients and lower bounds to the solubility of CaCl2in aqueous solutions at temperatures between 298.15 and 473.15 K using molecular simulations with three previously developed nonpolarizable force fields. We find that a scaled-charge force field gives incorrect activity coefficients at low concentration and has a different absolute chemical potential than experiments, but still predicts an accurate solubility for the calcium chloride dihydrate. The two full-charge models have chemical potentials and activity coefficients closer to experiments, but there is considerable variation between them, with the chemical potentials differing by over 100 kJ·mol−1. The slow dynamics of the full-charge models at high concentrations are unrealistic and require advanced sampling methods to obtain the activity coefficients. We find that development of polarizable models is likely necessary to accurately represent both thermodynamic and transport properties of divalent electrolyte solutions.

We investigate polymer precipitation dynamics using explicit-solvent dissipative particle dynamics. We present a method to partially exchange solvent by antisolvent. In a first set of simulations, we analyze the collapse of a single chain of 25 beads as both liquids are mixing. The variation of the chain’s mean-squared radius of gyration during collapse is found to be strongly influenced by the distance that the antisolvent must diffuse to the chain. This behavior is validated using an analytical model. We further report inclusion of solvent in the collapsed chains in equilibrium as well as the formation of a solvation layer. The influence of slip-springs and thus entanglements on the collapse of a single chain of 100 beads is evaluated in a second set of simulations. We did not find any influence of entanglements on the chain’s collapse nor can we report a two-stage collapse process when enforcing these self-entanglements onto the chain.

Histogram-reweighting grand canonical Monte Carlo simulations were used to obtain the phase behavior of CO2–H2O mixtures over a broad temperature and pressure range (50 °C ≤ T≤ 350 °C, 0 ≤ P≤ 1000 bar). We performed a comprehensive test of several existing water (SPC, TIP4P, TIP4P2005, and exponential-6) and carbon dioxide (EPM2, TraPPE, and exponential-6) models using conventional Lorentz–Berthelot combining rules for the unlike-pair parameters. None of the models we studied reproduce adequately experimental data over the entire temperature and pressure range, but critical assessments were made on the range of Tand Pwhere particular model pairs perform better. Away from the critical region (T≤ 250 °C), the exponential-6 model combination yields the best predictions for the CO2-rich phase, whereas the TraPPE/TIP4P2005 model combination provides the most accurate coexistence composition and pressure for the H2O-rich phase. Near the critical region (250 °C < T≤ 350 °C), the critical points are not accurately estimated by any of the models studied, but the exponential-6 models are able to qualitatively capture the critical loci and the shape of the phase envelopes. Local improvements can be achieved at specific temperatures by introducing modification factors to the Lorentz–Berthelot combining rules, but the modified combining rule is still not able to achieve global improvements over the entire temperature and pressure range. Our work points to the challenge and importance of improving current atomistic models so as to accurately predict the phase behavior of this important binary mixture.

Molecular dynamics simulations were performed to study the fabrication of polymeric colloids containing inorganic nanoparticles (NPs) via the flash nanoprecipitation (FNP) technique. During this process, a binary polymer blend, initially in a good solvent for the polymers, is rapidly mixed with NPs and a poor solvent for the polymers that is miscible with the good solvent. The simulations reveal that the polymers formed Janus particles with NPs distributed either on the surface of the aggregates, throughout their interior, or aligned at the interface between the two polymer domains, depending on the NP–polymer and NP–solvent interactions. The loading and surface density of NPs can be controlled by the polymer feed concentration, the NP feed concentration, and their ratio in the feed streams. Selective localization of NPs by incorporating electrostatic interactions between polymers and NPs has also been investigated, and was shown to be an effective way to enhance NP loading and surface density as compared to the case with only van der Waals attractions. This work demonstrates that the FNP process is promising for the production of structured and hybrid nanocolloids in a continuous and scalable way, with independent control over particle properties such as size, NP location, loading, and surface density. Our results provide useful guidelines for experimental fabrication of such hybrid nanoparticles.

We report a new implicit solvent simulation model for studying the self-assembly of surfactants, where the hydrophobic interactions were captured by calculating the relative changes of the solvent-accessible surface area (SASA) of the hydrophobic domains. Using histogram-reweighting grand canonical Monte Carlo simulations, we demonstrate that this approach allows us to match both the experimental critical micelle concentrations (cmc) and micellar aggregation numbers simultaneously with a single phenomenological surface tension γSASAfor the poly(oxyethylene) monoalkyl ether (CmEn) surfactants in aqueous solutions. Excellent transferability is observed: the same model can accurately predict the experimental cmc and aggregation numbers for the CmEnsurfactants with the alkyl lengths mbetween 6 and 12 and the poly(oxyethylene) lengths nbetween 1 and 9. The SASA-based implicit solvent model put forward in this work is general and may be applied to study more complex amphiphilic systems such as surfactants with branched alkyl chains or surfactant–hydrocarbon mixtures.

We study the stability of polymeric nanoparticles fabricated through the rapid mixing of polymers in a good solvent with a poor solvent that is miscible with the good solvent. In previous experiments where water was used as the poor solvent, a negative surface charge was measured on the precipitated nanoparticles, which led to the long-time stability of the dispersion. It was argued that these charges originate presumably from either water or hydroxide adsorption at the hydrophobic nanoparticle surface or from impurities in the feed streams that preferentially adsorb on the precipitated nanoparticles. To elucidate the origin of this stabilization mechanism, we performed experiments wherein we replaced water with a nonpolar poor solvent. The polymers aggregated into stable nanoparticles for a range of processing parameters. We investigated theoretically three possible explanations for this stability, i.e., electrostatic stabilization, conditional thermodynamic equilibrium, and steric stabilization. Our experiments and considerations suggest that steric stabilization is the most likely candidate.

We present an efficient open-source implementation of the multiparticle collision dynamics (MPCD) algorithm that scales to run on hundreds of graphics processing units (GPUs). We especially focus on optimizations for modern GPU architectures and communication patterns between multiple GPUs. We show that a mixed-precision computing model can improve performance compared to a fully double-precision model while still providing good numerical accuracy. We report weak and strong scaling benchmarks of a reference MPCD solvent and a benchmark of a polymer solution with research-relevant interactions and system size. Our MPCD software enables simulations of mesoscale hydrodynamics at length and time scales that would be otherwise challenging or impossible to access. [ABSTRACT FROM AUTHOR]

Colloids with internally structured geometries have shown great promise in applications ranging from biosensors to optics to drug delivery, where the internal particle structure is paramount to performance. The growing demand for such nanomaterials necessitates the development of a scalable processing platform for their production. Flash nanoprecipitation (FNP), a rapid and inherently scalable colloid precipitation technology, is used to prepare internally structured colloids from blends of block copolymers and homopolymers. As revealed by a combination of experiments and simulations, colloids prepared from different molecular weight diblock copolymers adopt either an ordered lamellar morphology consisting of concentric shells or a disordered lamellar morphology when chain dynamics are sufficiently slow to prevent defect annealing during solvent exchange. Blends of homopolymer and block copolymer in the feed stream generate more complex internally structured colloids, such as those with hierarchically structured Janus and patchy morphologies, due to additional phase separation and kinetic trapping effects. The ability of the FNP process to generate such a wide range of morphologies using a simple and scalable setup provides a pathway to manufacturing internally structured colloids on an industrial scale.

The structures formed by mixtures of dissimilarly shaped nanoscale objects can significantly enhance our ability to produce nanoscale architectures. However, understanding their formation is a complex problem due to the interplay of geometric effects (entropy) and energetic interactions at the nanoscale. Spheres and rods are perhaps the most basic geometrical shapes and serve as convenient models of such dissimilar objects. The ordered phases formed by each of these individual shapes have already been explored, however, when mixed, spheres and rods have demonstrated only limited structural organization to date. Here, we show using experiments and theory that the introduction of directional attractions between rod ends and isotropically interacting spherical nanoparticles (NPs) through DNA base pairing leads to the formation of ordered three-dimensional lattices. The spheres and rods arrange themselves in a complex alternating manner, where the spheres can form either a face-centered cubic (FCC) or hexagonal close-packed (HCP) lattice, or a disordered phase, as observed by in situX-ray scattering. Increasing NP diameter at fixed rod length yields an initial transition from a disordered phase to the HCP crystal, energetically stabilized by rod-rod attraction across alternating crystal layers, as revealed by theory. In the limit of large NPs, the FCC structure is instead stabilized over the HCP by rod entropy. We, therefore, propose that directionally specific attractions in mixtures of anisotropic and isotropic objects offer insight into unexplored self-assembly behavior of noncomplementary shaped particles.

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

In this work, we construct distinct first-principles-based machine-learning models of CO2, reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density functional theory. We employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initiomolecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid-phase configurations, they are able to simulate a stable interfacial system and predict vapor–liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for the liquid phase and vapor–liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

Globally ordered colloidal crystal lattices have broad utility in a wide range of optical and catalytic devices, for example, as photonic band gap materials. However, the self-assembly of stereospecific structures is often confounded by polymorphism. Small free-energy differences often characterize ensembles of different structures, making it difficult to produce a single morphology at will. Current techniques to handle this problem adopt one of two approaches: that of the “top-down” or “bottom-up” methodology, whereby structures are engineered starting from the largest or smallest relevant length scales, respectively. However, recently, a third approach for directing high fidelity assembly of colloidal crystals has been suggested which relies on the introduction of polymer cosolutes into the crystal phase [Mahynski, N.; Panagiotopoulos, A. Z.; Meng, D.; Kumar, S. K. Nat. Commun.2014, 5, 4472]. By tuning the polymer’s morphology to interact uniquely with the void symmetry of a single desired crystal, the entropy loss associated with polymer confinement has been shown to strongly bias the formation of that phase. However, previously, this approach has only been demonstrated in the limiting case of close-packed crystals. Here, we show how this approach may be generalized and extended to complex open crystals, illustrating the utility of this “structure-directing agent” paradigm in engineering the nanoscale structure of ordered colloidal materials. The high degree of transferability of this paradigm’s basic principles between relatively simple crystals and more complex ones suggests that this represents a valuable addition to presently known self-assembly techniques.

We studied the directed assembly of soft nanoparticles through rapid micromixing of polymers in solution with a nonsolvent. Both experiments and computer simulations were performed to elucidate the underlying physics and to investigate the role of various process parameters. In particular, we discovered that no external stabilizing agents or charged end groups are required to keep the colloids separated from each other when water is used as the nonsolvent. Furthermore, the size of the nanoparticles can be reliably tuned through the mixing rate and the ratio between polymer solution and nonsolvent. Our results demonstrate that this mechanism is highly promising for the mass fabrication of uniformly sized colloidal particles, using a wide variety of polymeric feed materials.

Molecular dynamics and Monte Carlo simulations were performed to obtain thermodynamic and transport properties of the binary H2O + NaCl system using the polarizable force fields of Kiss and Baranyai (J. Chem. Phys.2013, 138, 204507and 2014, 141, 114501). In particular, liquid densities, electrolyte and crystal chemical potentials of NaCl, salt solubilities, mean ionic activity coefficients, vapor pressures, vapor–liquid interfacial tensions, and viscosities were obtained as functions of temperature, pressure, and salt concentration. We compared the performance of the polarizable force fields against fixed-point-charge (nonpolarizable) models. Most of the properties of interest are better represented by the polarizable models, which also remain physically realistic at elevated temperatures.

We study thin films of homopolymers (PS) and monolayers of cylinder-forming diblock copolymers (PS–PHMA) under shear. To this end, we employed both experiments and computer simulations that correctly take into account hydrodynamic interactions and chain entanglements. Excellent quantitative agreement for static as well as dynamic properties in both the homopolymer and diblock copolymer cases was achieved. In particular, we found that the homopolymer thin films exhibit a distinct shear thinning behavior, which is strongly correlated with the disentanglement and shear alignment of the constituent polymer chains. For the PS–PHMA films, we show that shear can be employed to induce long-range ordering to the spontaneously self-assembled microdomains, which is required for many applications such as the fabrication of nanowire arrays. We found that the impact of chemical incompatibility on the viscosity is only minor in shear-aligned films. Once the domains were aligned, the films exhibited an almost Newtonian response to shear because the cylindrical microdomains acted as guide rails, along which the constituent copolymer chains could simply slide. Furthermore, we developed a model for predicting the onset of shear alignment based on equilibrium dynamics data, and found good agreement with our shear simulations. The employed computational method holds promise for a faster and more cost-effective route for developing custom tailored materials.

The antimicrobial peptide microcin J25 (MccJ25) is posttranslationally matured from a linear preprotein into its native lasso conformation by two enzymes. One of these enzymes cleaves the preprotein and the second enzyme installs the requisite isopeptide bond to establish the lasso structure. Analysis of a mimic of MccJ25 that can be cyclized without the influence of the maturation enzymes suggests that MccJ25 does not spontaneously adopt a near-lasso structure. In addition, we conducted atomistically detailed replica-exchange molecular dynamics simulations of pro-microcin J25 (pro-MccJ25), the 21-residue uncyclized analog of MccJ25, to determine the conformational ensemble explored in the absence of the leader sequence or maturation enzymes. We applied a nonlinear dimensionality reduction technique known as the diffusion map to the simulation trajectories to extract two global order parameters describing the fundamental dynamical motions of the system, and identify three distinct pathways. One path corresponds to the spontaneous adoption of a left-handed lasso, in which the N-terminus wraps around the C-terminus in the opposite sense to the right-handed topology of native MccJ25. Our computational and experimental results suggest a role for the MccJ25 leader sequence and/or its maturation enzymes in facilitating the adoption of the right-handed topology.

Abstract: This paper presents results from Monte Carlo (MC) and molecular dynamics (MD) simulations on the shear-induced long-ranged alignment of cylindrical micelles in thin films. The surfactant is represented on a lattice and the shear flow is simulated via incorporation of a shear-induced potential energy term within the acceptance criteria in the MC simulations. The MD simulations are conducted on a coarse-grained, off-lattice surfactant while the shear flow is imposed in thin films by sliding confining walls in opposite directions. It is shown that the two methods lead to different steady state orientations of micelles. We also discuss several problematic issues concerned with incorporating shear or dynamics within MC schemes. [Copyright &y& Elsevier]

The Gibbs ensemble simulation technique is used to investigate the ability of van der Waals 1-fluid theory to predict phase equilibria for binary Lennard-Jones mixtures. Simulation data for highly asymmetric mixtures with size and energy parameter ratios equal to 1.00, 0.5, 0.4, 0.35 and to 0.5, 0.33, 0.25 respectively are compared to theoretical results for cases in which unlike-pair interactions follow the Lorentz-Berthelot combining rules. Additional comparisons are made for vapour-liquid and liquid-liquid equilibria of mixtures with energy parameter deviating from the geometric mean (Berthelot) rule, and for vapour-liquid and gas-gas equilibria of mixtures with size parameter deviating from the arithmetic mean (Lorentz) rule. Good agreement was found between theory and simulation when the energy parameter deviates from the Berthelot combining rule. The agreement is less satisfactory when the size parameter deviates from the Lorentz rule, especially for the case of gas-gas equilibria.

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We present an algorithm based on linear bounding volume hierarchies (LBVHs) for computing neighbor (Verlet) lists using graphics processing units (GPUs) for colloidal systems characterized by large size disparities. We compare this to a GPU implementation of the current state-of-the-art CPU algorithm based on stenciled cell lists. We report benchmarks for both neighbor list algorithms in a Lennard-Jones binary mixture with synthetic interaction range disparity and a realistic colloid solution. LBVHs outperformed the stenciled cell lists for systems with moderate or large size disparity and dilute or semidilute fractions of large particles, conditions typical of colloidal systems. [ABSTRACT FROM AUTHOR]

Abstract: One- and two-stage free energy methods are common approaches for calculating the chemical potential from a molecular dynamics or Monte Carlo molecular simulation trajectory. Although these methods require significant amounts of CPU time spent on post-simulation analysis, this analysis step is well-suited for parallel execution. In this work, we implement this analysis step on graphics processing units (GPUs), an architecture that is optimized for massively parallel computation. A key issue in porting these free energy methods to GPUs is the trade-off between software efficiency and sampling efficiency. In particular, fixed performance costs in the software favor a higher number of insertion moves per configuration. However, higher numbers of moves lead to lower sampling efficiency. We explore this issue in detail, and find that for a dense, strongly interacting system of small molecules like liquid water, the optimal number of insertions per configuration can be as high as for a two-stage approach like Bennett’s method. We also find that our GPU implementation accelerates chemical potential calculations by as much as 60-fold when compared to an efficient, widely available CPU code running on a single CPU core. [Copyright &y& Elsevier]