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Chemical reactions and vapor–liquid equilibria for molten lithium hydroxide (LiOH) were studied using molecular dynamics simulations and a deep potential (DP) model. The neural network for the model was trained on quantum density functional theory data for a range of conditions. The DP model allows simulations over timescales of hundreds of ns, which provide equilibrium compositions for the systems of interest. Single-phase NPT simulations of the liquid show the decomposition of LiOH into lithium oxide (Li2O) and dissolved water (H2O). These DP results were validated by direct ab initio molecular dynamics simulations that confirmed the accuracy of the model with respect to reaction kinetics and equilibrium properties of the melt. The reactive vapor–liquid behavior of this system was subsequently studied using direct coexistence interfacial DP simulations. Partial pressures of H2O in the vapor are found to be in close agreement with available experimental measurements. By fitting temperature-dependent expressions for the reaction equilibrium and Henry's law constants, the equilibrium composition for any given initial composition and temperature can be quantitatively modeled. For high initial concentrations of Li2O or H2O, mixtures of LiOH + Li2O/H2O are found to undergo phase separation. The present study illustrates how DP-based molecular dynamics simulations can be used for quantitative modeling of multiphase reactive behavior with the accuracy of the underlying ab initio quantum chemical methods. [ABSTRACT FROM AUTHOR]

Histogram-reweighting grand canonical Monte Carlo simulations are used to obtain the critical properties of lattice chains composed of solvophilic and solvophobic monomers. The model is a modification of one proposed by Larson et al. [J. Chem. Phys. 83, 2411 (1985)], lowering the "contrast" between beads of different types to prevent aggregation into finite-size micelles that would mask true phase separation between bulk high- and low-density phases. Oligomeric chains of lengths between 5 and 24 beads are studied. Mixed-field finite-size scaling methods are used to obtain the critical properties with typical relative accuracies of better than 10−4 for the critical temperature and 10−3 for the critical volume fraction. Diblock chains are found to have lower critical temperatures and volume fractions relative to the corresponding homopolymers. The addition of solvophilic blocks of increasing length to a fixed-length solvophobic segment results in a decrease of both the critical temperature and the critical volume fraction, with an eventual slow asymptotic approach to the long-chain limiting behavior. Moving a single solvophobic or solvophilic bead along a chain leads to a minimum or maximum in the critical temperature, with no change in the critical volume fraction. Chains of identical length and composition have a significant spread in their critical properties, depending on their precise sequence. The present study has implications for understanding biomolecular phase separation and for developing design rules for synthetic polymers with specific phase separation properties. It also provides data potentially useful for the further development of theoretical models for polymer and surfactant phase behavior. [ABSTRACT FROM AUTHOR]

Phase separation of biomolecules can facilitate their spatiotemporally regulated self-assembly within living cells. Due to the selective yet dynamic exchange of biomolecules across condensate interfaces, condensates can function as reactive hubs by concentrating enzymatic components for faster kinetics. The principles governing this dynamic exchange between condensate phases, however, are poorly understood. In this work, we systematically investigate the influence of client–sticker interactions on the exchange dynamics of protein molecules across condensate interfaces. We show that increasing affinity between a model protein scaffold and its client molecules causes the exchange of protein chains between the dilute and dense phases to slow down and that beyond a threshold interaction strength, this slowdown in exchange becomes substantial. Investigating the impact of interaction symmetry, we found that chain exchange dynamics are also considerably slower when client molecules interact equally with different sticky residues in the protein. The slowdown of exchange is due to a sequestration effect, by which there are fewer unbound stickers available at the interface to which dilute phase chains may attach. These findings highlight the fundamental connection between client–scaffold interaction networks and condensate exchange dynamics. [ABSTRACT FROM AUTHOR]

Diamond is a promising material for multiple applications in quantum information processing and sensing as well as applications in microelectronics. However, diamond devices can be limited by surface defects that compromise charge stability and spin coherence, among others. Improved strategies in plasma etching of diamond could play an important role in minimizing or eliminating these defects. In this work, we explore plasma-assisted atomic scale etching of diamond using argon ions (Ar+), hydrogen ions (H+) and hydrogen atoms (H). We employ classical molecular dynamics (MD) simulations and test several interatomic potentials based on the Reactive Empirical Bond Order (REBO) form with comparisons to a variety of published experimental results. We performed MD simulations of low-energy hydrogen ( ⩽ 50 eV) and argon ( ⩽ 200 eV) ion bombardment of diamond surfaces. Ar+ bombardment can be used to locally smooth initially rough diamond surfaces via the formation of an amorphous C layer, the thickness of which increases with argon ion energy. Subsequent exposure with hydrogen ions (or fast neutrals) will selectively etch this amorphous C layer, leaving the underlying diamond layer mostly intact if the H energy is maintained below about 10 eV. The simulations suggest that combining Ar+ smoothing with selective, near threshold energy H removal of amorphous C can be an effective strategy for diamond surface engineering, leading to more reliable and sensitive diamond color center devices. [ABSTRACT FROM AUTHOR]

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology—a machine learning approach—to study this ubiquitous phase transition, starting from the phase diagram in the liquid–vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid–vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid–vapor coexistence and water cavitation. [ABSTRACT FROM AUTHOR]

This paper focuses on phase and aggregation behavior for linear chains composed of blocks of hydrophilic and hydrophobic segments. Phase and conformational transitions of patterned chains are relevant for understanding liquid–liquid separation of biomolecular condensates, which play a prominent role in cellular biophysics and for surfactant and polymer applications. Previous studies of simple models for multiblock chains have shown that, depending on the sequence pattern and chain length, such systems can fall into one of two categories: displaying either phase separation or aggregation into finite-size clusters. The key new result of this paper is that both formation of finite-size aggregates and phase separation can be observed for certain chain architectures at appropriate conditions of temperature and concentration. For such systems, a bulk dense liquid condenses from a dilute phase that already contains multi-chain finite-size aggregates. The computational approach used in this study involves several distinct steps using histogram-reweighting grand canonical Monte Carlo simulations, which are described in some level of detail. [ABSTRACT FROM AUTHOR]

The hypothesis that the anomalous behavior of liquid water is related to the existence of a second critical point in deeply supercooled states has long been the subject of intense debate. Recent, sophisticated experiments designed to observe the transformation between the two subcritical liquids on nano- and microsecond time scales, along with demanding numerical simulations based on classical (rigid) models parameterized to reproduce thermodynamic properties of water, have provided support to this hypothesis. A stronger numerical proof requires demonstrating that the critical point, which occurs at temperatures and pressures far from those at which the models were optimized, is robust with respect to model parameterization, specifically with respect to incorporating additional physical effects. Here, we show that a liquid–liquid critical point can be rigorously located also in the WAIL model of water [Pinnick et al., J. Chem. Phys. 137, 014510 (2012)], a model parameterized using ab initio calculations only. The model incorporates two features not present in many previously studied water models: It is both flexible and polarizable, properties which can significantly influence the phase behavior of water. The observation of the critical point in a model in which the water–water interaction is estimated using only quantum ab initio calculations provides strong support to the viewpoint according to which the existence of two distinct liquids is a robust feature in the free energy landscape of supercooled water. [ABSTRACT FROM AUTHOR]

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal. [ABSTRACT FROM AUTHOR]

Extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO2 and systematically assess how the strength and anisotropy of the CO2–CO2 interactions affect the models' ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO2, calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO2. These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram. [ABSTRACT FROM AUTHOR]

The formation of ice in the atmosphere affects precipitation and cloud properties, and plays a key role in the climate of our planet. Although ice can form directly from liquid water under deeply supercooled conditions, the presence of foreign particles can aid ice formation at much warmer temperatures. Over the past decade, experiments have highlighted the remarkable efficiency of feldspar minerals as ice nuclei compared to other particles present in the atmosphere. However, the exact mechanism of ice formation on feldspar surfaces has yet to be fully understood. Here, we develop a first-principles machine-learning model for the potential energy surface aimed at studying ice nucleation at microcline feldspar surfaces. The model is able to reproduce with high-fidelity the energies and forces derived from density-functional theory (DFT) based on the SCAN exchange and correlation functional. Our training set includes configurations of bulk supercooled water, hexagonal and cubic ice, microcline, and fully-hydroxylated feldspar surfaces exposed to a vacuum, liquid water, and ice. We apply the machine-learning force field to study different fully-hydroxylated terminations of the (100), (010), and (001) surfaces of microcline exposed to a vacuum. Our calculations suggest that terminations that do not minimize the number of broken bonds are preferred in a vacuum. We also study the structure of supercooled liquid water in contact with microcline surfaces, and find that water density correlations extend up to around 10 Å from the surfaces. Finally, we show that the force field maintains a high accuracy during the simulation of ice formation at microcline surfaces, even for large systems of around 30 000 atoms. Future work will be directed towards the calculation of nucleation free-energy barriers and rates using the force field developed herein, and understanding the role of different microcline surfaces in ice nucleation. [ABSTRACT FROM AUTHOR]

We obtain activity coefficients in NaCl and KCl solutions from implicit-water molecular dynamics simulations, at 298.15 K and 1 bar, using two distinct approaches. In the first approach, we consider ions in a continuum with constant relative permittivity (ɛr) equal to that of pure water; in the other approach, we take into account the concentration-dependence of ɛr, as obtained from explicit-water simulations. Individual ion activity coefficients (IIACs) are calculated using gradual insertion of single ions with uniform neutralizing backgrounds to ensure electroneutrality. Mean ionic activity coefficients (MIACs) obtained from the corresponding IIACs in simulations with constant ɛr show reasonable agreement with experimental data for both salts. Surprisingly, large systematic negative deviations are observed for both IIACs and MIACs in simulations with concentration-dependent ɛr. Our results suggest that the absence of hydration structure in implicit-water simulations cannot be compensated by correcting for the concentration-dependence of the relative permittivity ɛr. Moreover, even in simulations with constant ɛr for which the calculated MIACs are reasonable, the relative positioning of IIACs of anions and cations is incorrect for NaCl. We conclude that there are severe inherent limitations associated with implicit-water simulations in providing accurate activities of aqueous electrolytes, a finding with direct relevance to the development of electrolyte theories and to the use and interpretation of implicit-solvent simulations. [ABSTRACT FROM AUTHOR]

Liquid–liquid phase separation (LLPS) is widely utilized by the cell to organize and regulate various biochemical processes. Although the LLPS of proteins is known to occur in a sequence-dependent manner, it is unclear how sequence properties dictate the nature of the phase transition and thereby influence condensed phase morphology. In this work, we have utilized grand canonical Monte Carlo simulations for a simple coarse-grained model of disordered proteins to systematically investigate how sequence distribution, sticker fraction, and chain length impact the formation of finite-size aggregates, which can preempt macroscopic phase separation for some sequences. We demonstrate that a normalized sequence charge decoration (SCD) parameter establishes a "soft" predictive criterion for distinguishing when a model protein undergoes macroscopic phase separation vs finite aggregation. Additionally, we find that this order parameter is strongly correlated with the critical density for phase separation, highlighting an unambiguous connection between sequence distribution and condensed phase density. Results obtained from an analysis of the order parameter reveal that at sufficiently long chain lengths, the vast majority of sequences are likely to phase separate. Our results suggest that classical LLPS should be the primary phase transition for disordered proteins when short-ranged attractive interactions dominate and suggest a possible reason behind recent findings of widespread phase separation throughout living cells. [ABSTRACT FROM AUTHOR]

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor–liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor–liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor–liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. These results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram. [ABSTRACT FROM AUTHOR]

We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary. [ABSTRACT FROM AUTHOR]

Scaled-charge models have been recently introduced for molecular simulations of electrolyte solutions and molten salts to attempt to implicitly represent polarizability. Although these models have been found to accurately predict electrolyte solution dynamic properties, they have not been tested for coexistence properties, such as the vapor pressure of the melt. In this work, we evaluate the vapor pressure of a scaled-charge sodium chloride (NaCl) force field and compare the results against experiments and a non-polarizable full-charge force field. The scaled-charge force field predicts a higher vapor pressure than found in experiments, due to its overprediction of the liquid-phase chemical potential. Reanalyzing the trajectories generated from the scaled-charge model with full charges improves the estimation of the liquid-phase chemical potential but not the vapor pressure. [ABSTRACT FROM AUTHOR]

This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. The main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow the description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions. [ABSTRACT FROM AUTHOR]

Phase separation of intrinsically disordered proteins is important for the formation of membraneless organelles or biomolecular condensates, which play key roles in the regulation of biochemical processes within cells. In this work, we investigated the phase separation of different sequences of a coarse-grained model for intrinsically disordered proteins and discovered a surprisingly rich phase behavior. We studied both the fraction of total hydrophobic parts and the distribution of hydrophobic parts. Not surprisingly, sequences with larger hydrophobic fractions showed conventional liquid–liquid phase separation. The location of the critical point was systematically influenced by the terminal beads of the sequence due to changes in interfacial composition and tension. For sequences with lower hydrophobicity, we observed not only conventional liquid–liquid phase separation but also re-entrant phase behavior in which the liquid phase density decreases at lower temperatures. For some sequences, we observed the formation of open phases consisting of aggregates, rather than a normal liquid. These aggregates had overall lower densities than the conventional liquid phases and exhibited complex geometries with large interconnected string-like or membrane-like clusters. Our findings suggest that minor alterations in the ordering of residues may lead to large changes in the phase behavior of the protein, a fact of significant potential relevance for biology. [ABSTRACT FROM AUTHOR]

Two-phase simulations are commonly used to evaluate coexistence conditions, interfacial tensions, and other thermodynamic properties associated with first-order phase transitions. Calculation of these properties is often simplified when the interfaces between the two phases are flat or planar. Here, we derive a general thermodynamic criterion for selecting simulation cell dimensions to stabilize planar interfaces in phase-separated fluid-fluid systems with respect to homogeneous, single-phase states. The resulting expression is validated by analyzing the effects of simulation cell dimensions on the formation of planar liquid-vapor interfaces in the Lennard-Jones fluid and in the TIP4P/2005 model of water. We also perform large scale molecular dynamics simulations to study metastable liquid-liquid phase separation in the ST2 and TIP4P/2005 models of water under deeply supercooled conditions. Our simulations confirm the stability of a liquid-liquid interface in ST2, and they demonstrate that the corresponding interface for TIP4P/2005 can be stabilized by judiciously choosing the simulation cell aspect ratio in a manner consistent with the thermodynamic criterion. We posit that this sensitivity to the simulation cell aspect ratio may explain discrepancies between previous studies examining liquid-liquid separation in models of supercooled water. [ABSTRACT FROM AUTHOR]

In this work, we use large-scale molecular dynamics simulations coupled to free energy calculations to identify for the first time a limit of stability (spinodal) and a change in the nucleation mechanism in aqueous NaCl solutions. This is a system of considerable atmospheric, geological, and technical significance. We find that the supersaturated metastable NaCl solution reaches its limit of stability at sufficiently high salt concentrations, as indicated by the composition dependence of the salt chemical potential, indicating the transition to a phase separation by spinodal decomposition. However, the metastability limit of the NaCl solution does not correspond to spinodal decomposition with respect to crystallization. We find that beyond this spinodal, a liquid/amorphous separation occurs in the aqueous solution, whereby the ions first form disordered clusters. We term these clusters as "amorphous salt." We also identify a transition from one- to two-step crystallization mechanism driven by a spinodal. In particular, crystallization from aqueous NaCl solution beyond the spinodal is a two-step process, in which the ions first phase-separate into disordered amorphous salt clusters, followed by the crystallization of ions in the amorphous salt phase. By contrast, in the aqueous NaCl solution at concentrations lower than the spinodal, crystallization occurs via a one-step process as the ions aggregate directly into crystalline nuclei. The change of mechanism with increasing supersaturation underscores the importance of an accurate determination of the driving force for phase separation. The study has broader implications on the mechanism for nucleation of crystals from solutions at high supersaturations. [ABSTRACT FROM AUTHOR]

We present molecular dynamics simulations of the epitaxial growth of high quality crystalline films for photonics applications from triblock Janus colloids. With a featureless substrate, the film morphologies were qualitatively similar to previously reported experimental results, with two stacking polymorphs appearing in nearly equal proportion. However, with a patterned substrate deliberately designed to be easy to fabricate by standard photolithography techniques, both the grain size and selectivity towards the photonically active polymorph were greatly improved. We also evaluated the effect of particle flux to find that lower flux led to higher quality crystals, while higher flux led to frustrated films with smaller crystalline domains. Our results suggest that carefully engineered but simple to manufacture patterned substrates could yield self-assembled single crystals of sufficient quality to exhibit a complete photonic bandgap. [ABSTRACT FROM AUTHOR]

In this work, we use molecular dynamics simulations with a polarizable force field, namely, the modified AH/BK3 model [J. Kolafa, J. Chem. Phys. 145, 204509 (2016)], in combination with the forward flux sampling technique, to calculate the rates of homogeneous nucleation of NaCl from supersaturated aqueous solutions at 298 K and 1 bar. A non-polarizable model that reproduces the experimental equilibrium solubility {AH/TIP4P-2005 of Benavides et al. [J. Chem. Phys. 147, 104501 (2017)]} is also used for comparison. Nucleation rates calculated from the polarizable force field are found to be in good agreement with experimental measurements, while the non-polarizable model severely underestimates the nucleation rates. These results, in combination with our earlier study of a different non-polarizable force field [H. Jiang et al., J. Chem. Phys. 148, 044505 (2018)], lead to the conclusion that nucleation rates are sensitive to the details of force fields, and a good representation of nucleation rates may not be feasible using available non-polarizable force fields, even if these reproduce the equilibrium salt solubility. Inclusion of polarization could be important for an accurate prediction of nucleation rates in salt solutions. [ABSTRACT FROM AUTHOR]

Colloidal crystals are often prepared by evaporation from solution, and there is considerable interest to link the processing conditions to the crystal morphology and quality. Here, we study the evaporation-induced assembly of colloidal crystals using massive-scale nonequilibrium molecular dynamics simulations. We apply a recently developed machine-learning technique to characterize the assembling crystal structures with unprecedented microscopic detail. In agreement with previous experiments and simulations, faster evaporation rates lead to earlier onset of crystallization and more disordered surface structures. Surprisingly, we find that collective rearrangements of the bulk crystal during later stages of drying reduce the influence of the initial surface structure, and the final morphology is essentially independent of the evaporation rate. Our structural analysis reveals that the crystallization process is well-described by two time scales, the film drying time and the crystal growth time, with the latter having an unexpected dependence on the evaporation rate due to equilibrium thermodynamic effects at high colloid concentrations. These two time scales may be leveraged to control the relative influence of equilibrium and nonequilibrium growth mechanisms, suggesting a route to rapidly process colloidal crystals while also removing defects. Our analysis additionally reveals that solvent-mediated interactions play a critical role in the crystallization kinetics and that commonly used implicit-solvent models do not faithfully resolve nonequilibrium processes such as drying. [ABSTRACT FROM AUTHOR]

Using a multi-scale approach which combines both molecular dynamics (MD) and kinetic Monte Carlo (KMC) simulations, we study a simple and scalable method for fabricating charge-stabilized nanoparticles through a rapid solvent exchange, i.e., Flash NanoPrecipitation (FNP). This multi-scale approach is based on microscopic information from MD simulations and uses a KMC algorithm to access macroscopic length- and time scales, which allows direct comparison with experiments and quantitative predictions. We find good agreement of our simulation results with the experiments. In addition, the model allows us to understand the aggregation mechanism on both microscopic and macroscopic levels and determine dependence of nanoparticle size on processing parameters such as the mixing rate and the polymer feed concentration. It also provides an estimate for the characteristic growth time of nanoparticles in the FNP process. Our results thus give useful insights into tailoring the FNP technique for fabricating nanoparticles with a specific set of desirable properties for various applications. [ABSTRACT FROM AUTHOR]

Nonequilibrium molecular dynamics simulations are used to investigate the influence of hydrodynamic interactions on vertical segregation (stratification) in drying mixtures of long and short polymer chains. In agreement with previous computer simulations and theoretical modeling, the short polymers stratify above the long polymers at the top of the drying film when hydrodynamic interactions between polymers are neglected. However, no stratification occurs under the same drying conditions when hydrodynamic interactions are incorporated through an explicit solvent model. Our analysis demonstrates that models lacking hydrodynamic interactions do not faithfully represent stratification in drying mixtures, in agreement with the recent analysis of an idealized model for diffusiophoresis. Hydrodynamic interactions must be incorporated into such models for drying mixtures in future. [ABSTRACT FROM AUTHOR]

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates. [ABSTRACT FROM AUTHOR]

Molecular dynamic simulations are used to investigate the structural effects of treating a glassy polymer thin film with solvents of varying quality and subsequently evaporating the solvent. Both a monodisperse film and a polydisperse film are studied for poor to good solvent conditions, including the limit in which the polymer film is fully dissolved. In agreement with previous studies, the dissolved polymer-solvent mixtures form a polymer-rich skin on top of the forming film during evaporation. In the case of the polydisperse films, a segregation of the lower molecular weight polymer to the film interface is observed. We provide a detailed, systematic analysis of the interface structure and properties during and after evaporation. We find that for non-dissolved films, the surface width of the film after solvent evaporation is enhanced compared to the case without solvent. Our results show that due to the kinetic arrest of the surface structure, the increased surface width is preserved after solvent evaporation for both mono- and polydisperse films. We conclude that it is important to take poor solvent effects into account for the surface morphology of already formed thin glassy films, an effect which is often neglected. [ABSTRACT FROM AUTHOR]

We propose a method to calculate the equilibrium contact angle of heterogeneous 3-phase solid/fluid/fluid systems using molecular dynamics simulations. The proposed method, which combines the phantom-wall method [F. Leroy and F. Müller-Plathe, J. Chem. Phys. 133, 044110 (2010)] and Bennett's acceptance ratio approach [C. H. Bennett, J. Comput. Phys. 22, 245 (1976)], is able to calculate the solid/fluid surface tension relative to the solid surface energy. The calculated relative surface tensions can then be used in Young's equation to estimate the equilibrium contact angle. A fluid droplet is not needed for the proposed method, in contrast to the situation for direct simulations of contact angles. In addition, while prior free-energy based methods for contact angles mainly focused on the wetting of fluids in coexistence with their vapor on solid surfaces, the proposed approach was designed to study the contact angles of fluid mixtures on solid surfaces above the fluid saturation pressures. Using the proposed approach, the contact angles of binary Lennard-Jones fluid mixtures on a non-polar solid substrate were calculated at various interaction parameters and the contact angle of water in equilibrium with CO2 on a hydrophilic polar silica surface was obtained. For both nonpolar and polar systems, the calculated contact angles from the proposed method were in agreement with those obtained from the geometry of a cylindrical droplet. The computational cost of the proposed method was found to be comparable to that of simulations that use fluid droplets, but the new method provides a way to calculate the contact angle directly from Young's equation without ambiguity. [ABSTRACT FROM AUTHOR]

Salt water is ubiquitous, playing crucial roles in geological and physiological processes. Despite centuries of investigations, whether or not water's structure is drastically changed by dissolved ions is still debated. Based on density functional theory, we employ machine learning based molecular dynamics to model sodium chloride, potassium chloride, and sodium bromide solutions at different concentrations. The resulting reciprocal-space structure factors agree quantitatively with neutron diffraction data. Here we provide clear evidence that the ions in salt water do not distort the structure of water in the same way as neat water responds to elevated pressure. Rather, the computed structural changes are restricted to the ionic first solvation shells intruding into the hydrogen bond network, beyond which the oxygen radial-distribution function does not undergo major change relative to neat water. Our findings suggest that the widely cited pressure-like effect on the solvent in Hofmeister series ionic solutions should be carefully revisited. By advanced machine learning techniques, first-principles simulations find that dissolving salt in water does not change water structure drastically. It is contrary to the notion of "pressure effect" which has been widely applied over past 25 years. [ABSTRACT FROM AUTHOR]

Triblock Janus colloids, which are colloidal spheres decorated with attractive patches at each pole, have recently generated significant interest as potential building blocks for functional materials. Their inherent anisotropy is known to induce self-assembly into open structures at moderate temperatures and pressures, where they are stabilized over close-packed crystals by entropic effects. We present a numerical investigation of the equilibrium phases of triblock Janus particles with many different patch geometries in three dimensions, using Monte Carlo simulations combined with free energy calculations. In all cases, we find that the free energy difference between crystal polymorphs is less than 0.2 kBT per particle. By varying the patch fraction and interaction range, we show that large patches stabilize the formation of structures with four bonds per patch over those with three. This transition occurs abruptly above a patch fraction of 0.30 and has a strong dependence on the interaction range. Furthermore, we find that a short interaction range favors four bonds per patch, with longer range increasingly stabilizing structures with only three bonds per patch. By quantifying the effect of patch geometry on the stability of the equilibrium crystal structures, we provide insights into the fundamental design rules for constructing complex colloidal crystals. [ABSTRACT FROM AUTHOR]

Alternative methods for determining the critical micelle concentration (cmc) are investigated using canonical and grand canonical Monte Carlo simulations of a lattice surfactant model. A common measure of the cmc is the "free" (unassociated) surfactant concentration in the presence of micellar aggregates. Many prior simulations of micellizing systems have observed a decrease in the free surfactant concentration with overall surfactant loading for both ionic and nonionic surfactants, contrary to theoretical expectations from mass-action models of aggregation. In the present study, we investigate a simple lattice nonionic surfactant model in implicit solvent, for which highly reproducible simulations are possible in both the canonical (NVT) and grand canonical (μVT) ensembles. We confirm the previously observed decrease of free surfactant concentration at higher overall loadings and propose an algorithm for the precise calculation of the excluded volume and effective concentration of unassociated surfactant molecules in the accessible volume of the solution. We find that the cmc can be obtained by correcting the free surfactant concentration for volume exclusion effects resulting from the presence of micellar aggregates. We also develop an improved method for determination of the cmc based on the maximum in curvature for the osmotic pressure curve determined from μVT simulations. Excellent agreement in cmc and other micellar properties between NVT and μVT simulations of different system sizes is observed. The methodological developments in this work are broadly applicable to simulations of aggregating systems using any type of surfactant model (atomistic/coarse grained) or solvent description (explicit/implicit). [ABSTRACT FROM AUTHOR]

We study the effective interactions and phase behavior of star polymer-colloid mixtures through theory and Monte Carlo simulations. We extend previous theoretical approaches for calculating the effective star-colloid pair potential to take into account attractive contributions, which become significant at worsening solvent conditions. In order to assess the validity of our simulation and theory, we compute the effective interactions via virtual move parallel tempering Monte Carlo simulations using a microscopic bead-spring model for the star polymer and achieve excellent agreement. Finally, we perform grand canonical Monte Carlo simulations of the coarse-grained systems to study the effect of solvent quality on the phase behavior. [ABSTRACT FROM AUTHOR]

We perform hybrid molecular dynamics simulations to study the flow behavior of rigid colloids dispersed in a dilute polymer solution. The underlying Newtonian solvent and the ensuing hydrodynamic interactions are incorporated through multiparticle collision dynamics, while the constituent polymers are modeled as bead-spring chains, maintaining a description consistent with the colloidal nature of our system. We study the cross-stream migration of the solute particles in slit-like channels for various polymer lengths and colloid sizes and find a distinct focusing onto the channel center under specific solvent and flow conditions. To better understand this phenomenon, we systematically measure the effective forces exerted on the colloids. We find that the migration originates from a competition between viscoelastic forces from the polymer solution and hydrodynamically induced inertial forces. Our simulations reveal a significantly stronger fluctuation of the lateral colloid position than expected from thermal motion alone, which originates from the complex interplay between the colloid and polymer chains. [ABSTRACT FROM AUTHOR]

We investigate the thermodynamic behavior of a model polymer-grafted nanoparticle (GNP) system on a fine lattice, using grand canonical Monte Carlo simulations, to compare and contrast the validity of two different models for GNPs: "nanoparticle amphiphiles" versus "patchy particles." In the former model, continuous self-assembly processes are expected to dominate the system, whereas the latter are characterized by first-order phase separation into novel equilibrium phases such as "empty liquids." We find that, in general, considering GNPs as amphiphiles within the framework of a recent mean-field theory [Pryamtisyn et al., J. Chem. Phys. 131, 221102 (2009)] provides a qualitatively accurate description of the thermodynamics of GNP systems, revealing either first-order phase separation into two isotropic phases or continuous self-assembly. Our model GNPs display no signs of empty liquid formation, suggesting that these nanoparticles do not provide a route to such phases. [ABSTRACT FROM AUTHOR]

The mean ionic activity coefficients of aqueous NaCl solutions of varying concentrations at 298.15 K and 1 bar have been obtained from molecular dynamics simulations by gradually turning on the interactions of an ion pair inserted into the solution. Several common non-polarizable water and ion models have been used in the simulations. Gibbs-Duhem equation calculations of the thermodynamic activity of water are used to confirm the thermodynamic consistency of the mean ionic activity coefficients. While the majority of model combinations predict the correct trends in mean ionic activity coefficients, they overestimate their values at high salt concentrations. The solubility predictions also suffer from inaccuracies, with all models underpredicting the experimental values, some by large factors. These results point to the need for further ion and water model development. [ABSTRACT FROM AUTHOR]

Molecular dynamics and Monte Carlo simulations have been carried out to obtain thermodynamic and transport properties of the binary mixture H2O+NaCl at temperatures from T = 298 to 473 K. In particular, vapor pressures, liquid densities, viscosities, and vapor-liquid interfacial tensions have been obtained as functions of pressure and salt concentration. Several previously proposed fixedpoint-charge models that include either Lennard-Jones (LJ) 12-6 or exponential-6 (Exp6) functional forms to describe non-Coulombic interactions were studied. In particular, for water we used the SPC and SPC/E (LJ) models in their rigid forms, a semiflexible version of the SPC/E (LJ) model, and the Errington-Panagiotopoulos Exp6 model; for NaCl, we used the Smith-Dang and Joung-Cheatham (LJ) parameterizations as well as the Tosi-Fumi (Exp6) model. While none of the model combinations are able to reproduce simultaneously all target properties, vapor pressures are well represented using the SPC plus Joung-Cheathem model combination, and all LJ models do well for the liquid density, with the semiflexible SPC/E plus Joung-Cheatham combination being the most accurate. For viscosities, the combination of rigid SPC/E plus Smith-Dang is the best alternative. For interfacial tensions, the combination of the semiflexible SPC/E plus Smith-Dang or Joung-Cheatham gives the best results. Inclusion of water flexibility improves the mixture densities and interfacial tensions, at the cost of larger deviations for the vapor pressures and viscosities. The Exp6 water plus Tosi-Fumi salt model combination was found to perform poorly for most of the properties of interest, in particular being unable to describe the experimental trend for the vapor pressure as a function of salt concentration. [ABSTRACT FROM AUTHOR]

It has been recently suggested that hydroxide ions can be formed in the electrolyte of molten carbonate fuel cells when water vapor is present. The hydroxide can replace carbonate in transporting electrons across the electrolyte, thereby reducing the CO2 separation efficiency of the fuel cell although still producing electricity. In this work, we obtain the equilibrium concentration of hydroxide in five molten alkali carbonate salts from molecular simulations. The results reveal that there can be a substantial amount of hydroxide in the electrolyte at low partial pressures of CO2. In addition, we find that the equilibrium concentration of molecular water dissolved in the electrolyte is over two orders of magnitude higher than that of CO2. Increasing the size and polarizability (or in other words reducing the "hardness") of the cations present in the electrolyte can reduce the hydroxide fraction, but at the cost of lowering ionic conductivity. [ABSTRACT FROM AUTHOR]

We employ extensive computer simulations to study the flow behavior of spherical, nanoscale colloids in a viscoelastic solvent under Poiseuille flow. The systems are confined in a slit-like microfluidic channel, and viscoelasticity is introduced explicitly through the inclusion of polymer chains on the same length scale as the dispersed solute particles. We systematically study the effects of flow strength and polymer concentration, and identify a regime in which the colloids migrate to the centerline of the microchannel, expelling the polymer chains to the sides. This behavior was recently identified in experiments, but a detailed understanding of the underlying physics was lacking. To this end, we provide a detailed analysis of this phenomenon and discuss ways to maximize its effectiveness. The focusing mechanism can be exploited to separate and capture particles at the sub-micrometer scale using simple microfluidic devices, which is a crucial task for many biomedical applications, such as cell counting and genomic mapping. [ABSTRACT FROM AUTHOR]

In this contribution, we develop a coarse-graining methodology for mapping specific block copolymer systems to bead-spring particle-based models. We map the constituent Kuhn segments to Lennard-Jones particles, and establish a semi-empirical correlation between the experimentally determined Flory-Huggins parameter χ and the interaction of the model potential. For these purposes, we have performed an extensive set of isobaric-isothermal Monte Carlo simulations of binary mixtures of Lennard-Jones particles with the same size but with asymmetric energetic parameters. The phase behavior of these monomeric mixtures is then extended to chains with finite sizes through theoretical considerations. Such a top-down coarse-graining approach is important from a computational point of view, since many characteristic features of block copolymer systems are on time and length scales which are still inaccessible through fully atomistic simulations. We demonstrate the applicability of our method for generating parameters by reproducing the morphology diagram of a specific diblock copolymer, namely, poly(styrene-b-methyl methacrylate), which has been extensively studied in experiments. [ABSTRACT FROM AUTHOR]

We use the weighted histogram analysis method [S. Kumar, D. Bouzida, R. H. Swendsen, P. A. Kollman, and J. M. Rosenberg, J. Comput. Chem. 13, 1011 (1992)] to calculate the free energy surface of the ST2 model of water as a function of density and bond-orientational order. We perform our calculations at deeply supercooled conditions (T = 228.6 K, P = 2.2 kbar; T = 235 K, P = 2.2 kbar) and focus our attention on the region of bond-orientational order that is relevant to disordered phases. We find a first-order transition between a low-density liquid (LDL, ρ ≈ 0.9 g/cc) and a high-density liquid (HDL, ρ ≈ 1.15 g/cc), confirming our earlier sampling of the free energy surface of this model as a function of density [Y. Liu, A. Z. Panagiotopoulos, and P. G. Debenedetti, J. Chem. Phys. 131, 104508 (2009)]. We demonstrate the disappearance of the LDL basin at high pressure and of the HDL basin at low pressure, in agreement with independent simulations of the system's equation of state. Consistency between directly computed and reweighted free energies, as well as between free energy surfaces computed using different thermodynamic starting conditions, confirms proper equilibrium sampling. Diffusion and structural relaxation calculations demonstrate that equilibration of the LDL phase, which exhibits slow dynamics, is attained in the course of the simulations. Repeated flipping between the LDL and HDL phases in the course of long molecular dynamics runs provides further evidence of a phase transition. We use the Ewald summation with vacuum boundary conditions to calculate long-ranged Coulombic interactions and show that conducting boundary conditions lead to unphysical behavior at low temperatures. [ABSTRACT FROM AUTHOR]

The possible existence of a metastable liquid–liquid transition (LLT) and a corresponding liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal–multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP’s location. These combined results—obtained from a purely first-principles approach with no empirical parameters—are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms. [ABSTRACT FROM AUTHOR]

We investigated the dynamic properties of alkali halide solutions (NaCl, NaF, NaBr, NaI, LiCl, and KCl) using molecular dynamics simulations and several non-polarisable, polarisable, and scaled-charge models. The concentration dependence of shear viscosity was obtained with low statistical uncertainties to allow for calculation of the viscosity Jones-Dole B-coefficients. No prior values are available for the B-coefficients from molecular simulations of fully atomistic models for electrolyte solutions. In addition, we obtained diffusion coefficients with rigorous finite-size corrections to access ion mobilities; these provide insights on single ion hydration behaviour. We find that all models studied, even polarisable and scaled-charge models, quantitatively over-predict water structuring but qualitatively follow the experimentally determined Hofmeister series. All ion models considered are kosmotropes based on their calculated B-coefficient and diffusion coefficients, even for ions experimentally found to be chaotropes. These observations indicate that the water-ion interactions in these models are not adequately represented; additional interactions such as charge transfer must be incorporated in future models in order to better represent electrolyte solution properties. [ABSTRACT FROM AUTHOR]

Thin films of water under nanoscopic confinement are prevalent in natural and manufactured materials. To investigate the equilibrium and dynamic behavior of water in such environments, we perform molecular dynamics simulations of water confined between atomistically detailed hydrophobic plates at T = 298 K for pressures (-0.1) <= P <= 1.0 GPa and plate separations of 0.40 <= d <= 0.80 nm. From these simulations, we construct an expanded P-d phase diagram for confined water, and identify and characterize a previously unreported confined monolayer ice morphology. We also study the decompression-induced sublimation of bilayer ice in a d = 0.6 nm slit, employing principal component analysis to synthesize low-dimensional embeddings of the drying trajectories and develop insight into the sublimation mechanism. Drying is observed to proceed by the nucleation of a bridging vapor cavity at one corner of the crystalline slab, followed by expansion of the cavity along two edges of the plates, and the subsequent recession of the remaining promontory of bilayer crystal into the bulk fluid. Our findings have implications for the understanding of diverse phenomena in materials science, nanofluidics, and protein folding and aggregation. [ABSTRACT FROM AUTHOR]

Coarse-grained models of poly(ethylene oxide) oligomer-grafted nanoparticles are established by matching their structural distribution functions to atomistic simulation data. Coarse-grained force fields for bulk oligomer chains show excellent transferability with respect to chain lengths and temperature, but structure and dynamics of grafted nanoparticle systems exhibit a strong dependence on the core-core interactions. This leads to poor transferability of the core potential to conditions different from the state point at which the potential was optimized. Remarkably, coarse graining of grafted nanoparticles can either accelerate or slowdown the core motions, depending on the length of the grafted chains. This stands in sharp contrast to linear polymer systems, for which coarse graining always accelerates the dynamics. Diffusivity data suggest that the grafting topology is one cause of slower motions of the cores for short-chain oligomer-grafted nanoparticles; an estimation based on transition-state theory shows the coarse-grained core-core potential also has a slowing-down effect on the nanoparticle organic hybrid materials motions; both effects diminish as grafted chains become longer. [ABSTRACT FROM AUTHOR]

The diffusivity and structural relaxation characteristics of oligomer-grafted nanoparticles have been investigated with simulations of a previously proposed coarse-grained model at atmospheric pressure. Solvent-free, polymer-grafted nanoparticles as well as grafted nanoparticles in a melt were compared to a reference system of bare (ungrafted) particles in a melt. Whereas longer chains lead to a larger hydrodynamic radius and lower relative diffusivity for grafted particles in a melt, bulk solvent-free nanoparticles with longer chains have higher relative diffusivities than their short chain counterparts. Solvent-free nanoparticles with short chains undergo a glass transition as indicated by a vanishing diffusivity, diverging structural relaxation time and the formation of body-centered-cubic-like order. Nanoparticles with longer chains exhibit a more gradual increase in the structural relaxation time with decreasing temperature and concomitantly increasing particle volume fraction. The diffusivity of the long chain nanoparticles exhibits a minimum at an intermediate temperature and volume fraction where the polymer brushes of neighboring particles overlap, but must stretch to fill the interparticle space. [ABSTRACT FROM AUTHOR]

Dissipative particle dynamics simulations were used to study the effects of mixing time, solute solubility, solute and diblock copolymer concentrations, and copolymer block length on the rapid coprecipitation of polymer-protected nanoparticles. The simulations were aimed at modeling Flash NanoPrecipitation, a process in which hydrophobic solutes and amphiphilic block copolymers are dissolved in a water-miscible organic solvent and then rapidly mixed with water to produce composite nanoparticles. A previously developed model by Spaeth et al. [J. Chem. Phys. 134, 164902 (2011)] was used. The model was parameterized to reproduce equilibrium and transport properties of the solvent, hydrophobic solute, and diblock copolymer. Anti-solvent mixing was modeled using time-dependent solvent-solute and solvent-copolymer interactions. We find that particle size increases with mixing time, due to the difference in solute and polymer solubilities. Increasing the solubility of the solute leads to larger nanoparticles for unfavorable solute-polymer interactions and to smaller nanoparticles for favorable solute-polymer interactions. A decrease in overall solute and polymer concentration produces smaller nanoparticles, because the difference in the diffusion coefficients of a single polymer and of larger clusters becomes more important to their relative rates of collisions under more dilute conditions. An increase in the solute-polymer ratio produces larger nanoparticles, since a collection of large particles has less surface area than a collection of small particles with the same total volume. An increase in the hydrophilic block length of the polymer leads to smaller nanoparticles, due to an enhanced ability of each polymer to shield the nanoparticle core. For unfavorable solute-polymer interactions, the nanoparticle size increases with hydrophobic block length. However, for favorable solute-polymer interactions, nanoparticle size exhibits a local minimum with respect to the hydrophobic block length. Our results provide insights on ways in which experimentally controllable parameters of the Flash NanoPrecipitation process can be used to influence aggregate size and composition during self-assembly. [ABSTRACT FROM AUTHOR]

We have developed explicit- and implicit-solvent models for the flash nanoprecipitation process, which involves rapid coprecipitation of block copolymers and solutes by changing solvent quality. The explicit-solvent model uses the dissipative particle dynamics (DPD) method and the implicit-solvent model uses the Brownian dynamics (BD) method. Each of the two models was parameterized to match key properties of the diblock copolymer (specifically, critical micelle concentration, diffusion coefficient, polystyrene melt density, and polyethylene glycol radius of gyration) and the hydrophobic solute (aqueous solubility, diffusion coefficient, and solid density). The models were simulated in the limit of instantaneous mixing of solvent with antisolvent. Despite the significant differences in the potentials employed in the implicit- and explicit-solvent models, the polymer-stabilized nanoparticles formed in both sets of simulations are similar in size and structure; however, the dynamic evolution of the two simulations is quite different. Nanoparticles in the BD simulations have diffusion coefficients that follow Rouse behavior (D ∝ M-1), whereas those in the DPD simulations have diffusion coefficients that are close to the values predicted by the Stokes-Einstein relation (D ∝ R-1). As the nanoparticles become larger, the discrepancy between diffusion coefficients grows. As a consequence, BD simulations produce increasingly slower aggregation dynamics with respect to real time and result in an unphysical evolution of the nanoparticle size distribution. Surface area per polymer of the stable explicit-solvent nanoparticles agrees well with experimental values, whereas the implicit-solvent nanoparticles are stable when the surface area per particle is roughly two to four times larger. We conclude that implicit-solvent models may produce questionable results when simulating nonequilibrium processes in which hydrodynamics play a critical role. [ABSTRACT FROM AUTHOR]

Nonlinear dimensionality reduction techniques can be applied to molecular simulation trajectories to systematically extract a small number of variables with which to parametrize the important dynamical motions of the system. For molecular systems exhibiting free energy barriers exceeding a few kBT, inadequate sampling of the barrier regions between stable or metastable basins can lead to a poor global characterization of the free energy landscape. We present an adaptation of a nonlinear dimensionality reduction technique known as the diffusion map that extends its applicability to biased umbrella sampling simulation trajectories in which restraining potentials are employed to drive the system into high free energy regions and improve sampling of phase space. We then propose a bootstrapped approach to iteratively discover good low-dimensional parametrizations by interleaving successive rounds of umbrella sampling and diffusion mapping, and we illustrate the technique through a study of alanine dipeptide in explicit solvent. [ABSTRACT FROM AUTHOR]

We perform histogram-reweighting grand canonical Monte Carlo simulations of the Lennard-Jones fluid confined between two parallel hard walls and determine the vapor-liquid critical and coexistence properties in the range of σ≤H≤6σ and 10σ≤Lx,Ly≤28σ, where H is the wall separation, Lx=Ly is the system size and σ is the characteristic length. By matching the probability distribution of the ordering operator, P(M), to the three-dimensional (3D) and two-dimensional (2D) Ising universality classes according to the mixed-field finite-size scaling approach, we establish a “phase diagram” in the (H,L) plane, showing the boundary between four types of behavior: 3D, quasi-3D, quasi-2D, and 2D. In order to facilitate 2D critical point calculation, we present a four-parameter analytical expression for the 2D Ising universal distribution. We show that the infinite-system-size critical points obtained by extrapolation from the apparent 3D and 2D critical points have only minor differences with each other. In agreement with recent reports in the literature [Jana et al., J. Chem. Phys. 130, 214707 (2009)], we find departure from linearity in the relationship between critical temperature and inverse wall separation, as well as nonmonotonic dependence of the critical density and the liquid density at coexistence upon wall separation. Additional studies of the ST2 model of water show similar behavior, which suggests that these are quite general properties of confined fluids. [ABSTRACT FROM AUTHOR]

We use molecular dynamics simulations over microsecond time scales to study the micellization behavior of recently proposed continuum-space, coarse grained surfactant models. In particular, we focus on the MARTINI model by Marrink et al. [J. Phys. Chem. B 111, 7812 (2007)] and a model by Shinoda et al. [Soft Matter 4, 2454 (2008)]. We obtain the critical micelle concentration (cmc) and equilibrium aggregate size distributions at low surfactant loadings. We present evidence justifying modest extrapolations for determining the cmc at low temperatures, where significant sampling difficulties remain. The replica exchange method provides only modest improvements of sampling efficiency for these systems. We find that the two coarse grained models significantly underpredict experimental cmc near room temperature for zwitterionic surfactants, but are closer to measured values for nonionic ones. The aggregation numbers for both zwitterionic and nonionic surfactants are near those observed experimentally, but the temperature dependence of the cmc is incorrect in both cases, because of the use of an unstructured solvent. Possible refinements to the models to bring them into quantitative agreement with experiment are discussed. [ABSTRACT FROM AUTHOR]

Recent experimental results demonstrated that polymer grafted nanoparticles in solvents display self-assembly behavior similar to the microphase separation of block copolymers and other amphiphiles. We present a mean-field theory and complementary computer simulations to shed light on the parametric underpinnings of the experimental observations. Our theory suggests that such self-assembled structures occur most readily when the nanoparticle size is comparable to the radius of gyration of the polymer brush chains. Much smaller particle sizes are predicted to yield uniform particle dispersions, while larger particles are expected to agglomerate due to phase separation from the solvent. Selected aspects of our theoretical predictions are corroborated by computer simulations. [ABSTRACT FROM AUTHOR]