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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]

Liquid water's isothermal compressibility (1) and isobaric heat capacity (2), and the magnitude of its thermal expansion coefficient (3), increase sharply on cooling below the equilibrium freezing point. Many experimental [...]

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]