Water modeled as an intermediate element between carbon and silicon

The Mechanism of Heterogeneous Ice Nucleation by Fatty Alcohol Monolayers

Organic ice nucleating substances (INSs) are thought to play an essential role in cloud formation and, hence, precipitation and climate. Organic INSs are an important but poorly understood class of INSs in the atmosphere. To study organic INSs with exposed hydroxylated surfaces, researchers have previously used fatty alcohol monolayers as model systems. For alcohol monolayers, ice nucleation temperatures increase with increasing alkyl chain length and show a high-low oscillation following the number (odd-even) of carbon atoms in the alkyl chains. We employ atomistic models, together with molecular dynamics simulations, to investigate ice nucleation by C20H41OH, C30H61OH, and C31H63OH monolayers. As expected, we find that ice nucleation by alcohol monolayers depends on the lattice match to ice, and a poorer lattice match can at least partially account for the reduced ice nucleation ability of C20H41OH monolayers compared to monolayers of the longer chain alcohols. More interestingly, our simulations identify a limited range of alcohol configurations that readily nucleate ice via the basal plane. For configurations outside this range, ice nucleation did not occur on the time scale of our simulations (i.e., 5000 ns). The configurational feature that crucially influences ice nucleation is the angle between the alcohol C-O bond and the interfacial plane. C-O bonds directed sharply toward or away from the water phase strongly inhibit ice nucleation. In contrast, ice nucleation is easily observed for a relatively narrow band of C-O bond orientations centered about the surface plane. For comparable surface configurations, the ice nucleating abilities of C30H61OH and C31H63OH monolayers are practically identical, but the existence of a narrow band of ice-compatible surface configurations can perhaps explain why odd-chain alcohol monolayers are better INSs than even-chain alcohol monolayers. Earlier simulations have shown that for alcohols differing by a single carbon atom, the odd-chain monolayer is less rigid than the even-chain monolayer. This suggests the possibility that for odd-chain alcohol monolayers, the orientation of the C-O bonds can more easily adjust into the ice-compatible range than their even-chain counterparts, accounting for their enhanced ice nucleating ability.

Bridging classical nucleation theory and molecular dynamics simulation for homogeneous ice nucleation

Water freezing, initiated by ice nucleation, occurs widely in nature, ranging from cellular to global phenomena. Ice nucleation has been experimentally proven to require the formation of a critical ice nucleus, consistent with classical nucleation theory (CNT). However, the accuracy of CNT quantitative predictions of critical cluster sizes and nucleation rates has never been verified experimentally. In this study, we circumvent this difficulty by using molecular dynamics (MD) simulation. The physical properties of water/ice for CNT predictions, including density, chemical potential difference, and diffusion coefficient, are independently obtained using MD simulation, whereas the calculation of interfacial free energy is based on thermodynamic assumptions of CNT, including capillarity approximation among others. The CNT predictions are compared to the MD evaluations of brute-force simulations and forward flux sampling methods. We find that the CNT and MD predicted critical cluster sizes are consistent, and the CNT predicted nucleation rates are higher than the MD predicted values within three orders of magnitude. We also find that the ice crystallized from supercooled water is stacking-disordered ice with a stacking of cubic and hexagonal ices in four representative types of stacking. The prediction discrepancies in nucleation rate mainly arise from the stacking-disordered ice structure, the asphericity of ice cluster, the uncertainty of ice-water interfacial free energy, and the kinetic attachment rate. Our study establishes a relation between CNT and MD to predict homogeneous ice nucleation.

Correlating Ultrastability with Fragility and Surface Mobility in Vapor Deposited Tetrahedral Glasses

Several experiments on molecular and metallic glasses have shown that the ability of vapor deposition to produce ultrastable glasses is correlated to their structural and thermodynamic properties. Here we investigate the vapor deposition of a class of tetrahedral materials (including silicon and water) via molecular dynamics simulations of the generalized Stillinger-Weber potential. By changing a single parameter that controls the local tetrahedrality, we show that the emergence of ultrastable behavior is correlated with an increase in the fragility of the model. At the same time, while the mobility of the surface compared to the bulk shows only slight changes at low temperature, with increasing the tetrahedrality, it displays a significant enhancement toward the glass transition temperature. Our results point toward a strong connection between bulk dynamics, surface dynamics, and glass-ultrastability ability in this class of materials.

Impingement of binary nanodroplets on rough surfaces: a molecular dynamics study

Roughness or texture endow the solid surface with the ability of some particular property of water repellency that has been employed in a variety of practical applications, including self-cleaning, icing-resistant, and so forth. However, the understanding of the dynamic evolution of impacting binary droplets on rough surfaces is not satisfactory, especially at the nanoscale. In this work, we investigate the impact process of the binary droplet system, a suspending droplet impacts a sessile one deposited on hydrophobic textured surfaces, via molecular dynamics (MD) simulations. Dynamic evolutions from MD simulations under various impact conditions are discussed, including coalescence, spreading, retraction and vibration, and bouncing. The free energy variation during the impacting process is calculated to reveal the mechanisms behind the impact dynamics. The effect of the surface texture on the spreading and retraction is investigated, and the corresponding maximum spreading diameter is also discussed. Finally, we investigate the effect of the surface texture on bouncing behavior, which is found to promote the droplet bouncing at low We range but suppress the bouncing behavior at high We range.

Unraveling Ice-Solid Interface Rupture Dynamics: Insights from Molecular Dynamics Simulations

Unwanted icing on exposed surfaces poses significant risks, driving the quest for effective anti-icing mechanisms. While fracture mechanics concepts have been developed for designing coatings that weaken the ice-solid interface on soft surfaces, the factors that dictate ice adhesion strength and its counterpart, ice removal force, on hard surfaces remain poorly understood. In this study, we employ molecular dynamics simulations to investigate the interface rupture between ice and a hard solid substrate. The results indicate that the ice adhesion strength is contingent on the length of the ice cube. By examining the shearing behavior, we reveal a nanoscale critical force-bearing length. The shear force required to detach the ice scales proportionally with the length of the ice cube when it is smaller than the critical length. Once the ice cube length exceeds the critical length, the shear force stabilizes at a constant maximum value, revealing the existence of a maximum ice-removal force. The results align with the so-called strength versus toughness-controlled deicing regimes and are in agreement with cohesive zone modeling at the continuum length scale and recent experimental results. Our results extend this understanding to the nanoscale, confirming consistency between macro and micro scales. This consistency suggests that the toughness of the ice-solid interface is intrinsically governed by ice-surface interactions. By unraveling key intrinsic factors and their scale-dependent effects on the interface rupture of ice on surfaces, this study lays a solid theoretical foundation for the design and fabrication of next-generation anti-icing surfaces.

Ice Nucleation Mechanisms on Platinum Surfaces in PEM Fuel Cells: Effects of Surface Morphology and Wettability

Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL.

Accuracy limit of non-polarizable four-point water models: TIP4P/2005 vs OPC. Should water models reproduce the experimental dielectric constant?

The last generation of four center non-polarizable models of water can be divided into two groups: those reproducing the dielectric constant of water, as OPC, and those significantly underestimating its value, as TIP4P/2005. To evaluate the global performance of OPC and TIP4P/2005, we shall follow the test proposed by Vega and Abascal in 2011 evaluating about 40 properties to fairly address this comparison. The liquid-vapor and liquid-solid equilibria are computed, as well as the heat capacities, isothermal compressibilities, surface tensions, densities of different ice polymorphs, the density maximum, equations of state at high pressures, and transport properties. General aspects of the phase diagram are considered by comparing the ratios of different temperatures (namely, the temperature of maximum density, the melting temperature of hexagonal ice, and the critical temperature). The final scores are 7.2 for TIP4P/2005 and 6.3 for OPC. The results of this work strongly suggest that we have reached the limit of what can be achieved with non-polarizable models of water and that the attempt to reproduce the experimental dielectric constant deteriorates the global performance of the water force field. The reason is that the dielectric constant depends on two surfaces (potential energy and dipole moment surfaces), whereas in the absence of an electric field, all properties can be determined simply from just one surface (the potential energy surface). The consequences of the choice of the water model in the modeling of electrolytes in water are also discussed.

Surface-induced water crystallisation driven by precursors formed in negative pressure regions

Ice nucleation is a crucial process in nature and industries; however, the role of the free surface of water in this process remains unclear. To address this, we investigate the microscopic freezing process using brute-force molecular dynamics simulations. We discover that the free surface assists ice nucleation through an unexpected mechanism. The surface-induced negative pressure enhances the formation of local structures with a ring topology characteristic of Ice 0-like symmetry, promoting ice nucleation despite the symmetry differing from ordinary ice crystals. Unlike substrate-induced nucleation via water-solid interactions that occurs directly on the surface, this negative-pressure-induced mechanism promotes ice nucleation slightly inward the surface. Our findings provide a molecular-level understanding of the mechanism and pathway behind free-surface-induced ice formation, resolving the longstanding debate. The implications of our discoveries are of substantial importance in areas such as cloud formation, food technology, and other fields where ice nucleation plays a pivotal role.

Molecular-Scale Liquid Density Fluctuations and Cavity Thermodynamics

Equilibrium density fluctuations at the molecular level produce cavities in a liquid and can be analyzed to shed light on the statistics of the number of molecules occupying observation volumes of increasing radius. An information theory approach led to the conclusion that these probabilities should follow a Gaussian distribution. Computer simulations confirmed this prediction across various liquid models if the size of the observation volume is not large. The reversible work required to create a cavity and the chance of finding no molecules in a fixed observation volume are directly correlated. The Gaussian formula for the latter probability is scrutinized to derive the changes in enthalpy and entropy, which arise from the cavity creation. The reversible work of cavity creation has a purely entropic origin as a consequence of the solvent-excluded volume effect produced by the inaccessibility of a region of the configurational space. The consequent structural reorganization leads to a perfect compensation of enthalpy and entropy changes. Such results are coherent with those obtained from Lee in his direct statistical mechanical study.

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

We develop ∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently released HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

Two-dimensional bilayer ice in coexistence with three-dimensional ice without confinement

Icing plays an important role in various physical-chemical process. Although the formation of two-dimensional ice requires nanoscale confinement, two-dimensional bilayer ice in coexistence with three-dimensional ice without confinement remains poorly understood. Here, a critical value of a surface energy parameter is identified to characterize the liquid-solid interface interaction, above which two-dimensional and three-dimensional coexisting ice can surprisingly form on the surface. The two-dimensional ice growth mechanisms could be revealed by capturing the growth and merged of the metastable edge structures. The phase diagram about temperature and pressure vs energy parameters is predicted to distinguish liquid water, two-dimensional ice and three-dimensional ice. Furthermore, the deicing characteristics of coexisting ice demonstrate that the ice adhesion strength is linearly related to the ratio of ice-surface interaction energy to ice temperature. In addition, for gas-solid phase transition, the phase diagram about temperature and energy parameters is predicted to distinguish gas, liquid water, two-dimensional ice and three-dimensional ice. This work gives a perspective for studying the singular structure and dynamics of ice in nanoscale and provides a guide for future experimental realization of the coexisting ice.

Bioinspired Flexible Hydrogelation with Programmable Properties for Tactile Sensing

Tactile sensing requires integrated detection platforms with distributed and highly sensitive haptic sensing capabilities along with biocompatibility, aiming to replicate the physiological functions of the human skin and empower industrial robotic and prosthetic wearers to detect tactile information. In this regard, short peptide-based self-assembled hydrogels show promising potential to act as bioinspired supramolecular substrates for developing tactile sensors showing biocompatibility and biodegradability. However, the intrinsic difficulty to modulate the mechanical properties severely restricts their extensive employment. Herein, by controlling the self-assembly of 9-fluorenylmethoxycarbonyl-modifid diphenylalanine (Fmoc-FF) through introduction of polyethylene glycol diacrylate (PEGDA), wider nanoribbons are achieved by untwisting from well-established thinner nanofibers, and the mechanical properties of the supramolecular hydrogels can be enhanced 10-fold, supplying bioinspired supramolecular encapsulating substrate for tactile sensing. Furthermore, by doping with poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and 9-fluorenylmethoxycarbonyl-modifid 3,4-dihydroxy-l-phenylalanine (Fmoc-DOPA), the Fmoc-FF self-assembled hydrogels can be engineered to be conductive and adhesive, providing bioinspired sensing units and adhesive layer for tactile sensing applications. Therefore, the integration of these modules results in peptide hydrogelation-based tactile sensors, showing high sensitivity and sustainable responses with intrinsic biocompatibility and biodegradability. The findings establish the feasibility of developing programmable peptide self-assembly with adjustable features for tactile sensing applications.

Imaging surface structure and premelting of ice Ih with atomic resolution

Ice surfaces are closely relevant to many physical and chemical properties, such as melting, freezing, friction, gas uptake and atmospheric reaction1-8. Despite extensive experimental and theoretical investigations9-17, the exact atomic structures of ice interfaces remain elusive owing to the vulnerable hydrogen-bonding network and the complicated premelting process. Here we realize atomic-resolution imaging of the basal (0001) surface structure of hexagonal water ice (ice Ih) by using qPlus-based cryogenic atomic force microscopy with a carbon monoxide-functionalized tip. We find that the crystalline ice-Ih surface consists of mixed Ih- and cubic (Ic)-stacking nanodomains, forming 19 × 19 periodic superstructures. Density functional theory reveals that this reconstructed surface is stabilized over the ideal ice surface mainly by minimizing the electrostatic repulsion between dangling OH bonds. Moreover, we observe that the ice surface gradually becomes disordered with increasing temperature (above 120 Kelvin), indicating the onset of the premelting process. The surface premelting occurs from the defective boundaries between the Ih and Ic domains and can be promoted by the formation of a planar local structure. These results put an end to the longstanding debate on ice surface structures and shed light on the molecular origin of ice premelting, which may lead to a paradigm shift in the understanding of ice physics and chemistry.

The multi-scale polarizable pseudo-particle solvent coarse-grained approach: From NaCl salt solutions to polyelectrolyte hydration

We discuss key parameters that affect the reliability of hybrid simulations in the aqueous phase based on an efficient multi-scale coarse-grained polarizable pseudo-particle approach, denoted as pppl, to model the solvent water, whereas solutes are modeled using an all atom polarizable force field. Among those parameters, the extension of the solvent domain (SD) at the solute vicinity (domain in which each solvent particle corresponds to a single water molecule) and the magnitude of solute/solvent short range polarization damping effects are shown to be pivotal to model NaCl salty aqueous solutions and the hydration of charged systems, such as the hydrophobic polyelectrolyte polymer that we have recently investigated [Masella et al., J. Chem. Phys. 155, 114903 (2021)]. Strong short range damping is pivotal to simulate aqueous salt NaCl solutions at moderate concentration (up to 1.0M). The SD extension (as well as short range damping) has a weak effect on the polymer conformation; however, it plays a pivotal role in computing accurate polymer/solvent interaction energies. As the pppl approach is up to two orders of magnitude computationally more efficient than all atom polarizable force field methods, our results show it to be an efficient alternative route to investigate the equilibrium properties of complex charged molecular systems in extended chemical environments.

Mesomorphology of clathrate hydrates from molecular ordering

Clathrate hydrates are crystals formed by guest molecules that stabilize cages of hydrogen-bonded water molecules. Whereas thermodynamic equilibrium is well described via the van der Waals and Platteeuw approach, the increasing concerns with global warming and energy transition require extending the knowledge to non-equilibrium conditions in multiphase, sheared systems, in a multiscale framework. Potential macro-applications concern the storage of carbon dioxide in the form of clathrates, and the reduction of hydrate inhibition additives currently required in hydrocarbon production. We evidence porous mesomorphologies as key to bridging the molecular scales to macro-applications of low solubility guests. We discuss the coupling of molecular ordering with the mesoscales, including (i) the emergence of porous patterns as a combined factor from the walk over the free energy landscape and 3D competitive nucleation and growth and (ii) the role of molecular attachment rates in crystallization-diffusion models that allow predicting the timescale of pore sealing. This is a perspective study that discusses the use of discrete models (molecular dynamics) to build continuum models (phase field models, crystallization laws, and transport phenomena) to predict multiscale manifestations at a feasible computational cost. Several advances in correlated fields (ice, polymers, alloys, and nanoparticles) are discussed in the scenario of clathrate hydrates, as well as the challenges and necessary developments to push the field forward.

Threshold current density for diffusion-controlled stability of electrolytic surface nanobubbles

Understanding the stability mechanism of surface micro/nanobubbles adhered to gas-evolving electrodes is essential for improving the efficiency of water electrolysis, which is known to be hindered by the bubble coverage on electrodes. Using molecular simulations, the diffusion-controlled evolution of single electrolytic nanobubbles on wettability-patterned nanoelectrodes is investigated. These nanoelectrodes feature hydrophobic islands as preferential nucleation sites and allow the growth of nanobubbles in the pinning mode. In these simulations, a threshold current density distinguishing stable nanobubbles from unstable nanobubbles is found. When the current density remains below the threshold value, nucleated nanobubbles grow to their equilibrium states, maintaining their nanoscopic size. However, for the current density above the threshold value, nanobubbles undergo unlimited growth and can eventually detach due to buoyancy. Increasing the pinning length of nanobubbles increases the degree of nanobubble instability. By connecting the current density with the local gas oversaturation, an extension of the stability theory for surface nanobubbles [Lohse and Zhang, Phys. Rev. E 91, 031003(R) (2015)] accurately predicts the nanobubble behavior found in molecular simulations, including equilibrium contact angles and the threshold current density. For larger systems that are not accessible to molecular simulations, continuum numerical simulations with the finite difference method combined with the immersed boundary method are performed, again demonstrating good agreement between numerics and theories.

Liquid-liquid transition and ice crystallization in a machine-learned coarse-grained water model

Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water has impeded identification of the LLT line TLL(p) in experiments. While the most accurate all-atom (AA) water models display a LLT, their computational cost limits investigations of its interplay with ice formation. Coarse-grained (CG) models provide over 100-fold computational efficiency gain over AA models, enabling the study of water crystallization, but have not yet shown to have a LLT. Here, we demonstrate that the CG machine-learned water model Machine-Learned Bond-Order Potential (ML-BOP) has a LLT that ends in a critical point at pc = 170 ± 10 MPa and Tc = 181 ± 3 K. The TLL(p) of ML-BOP is almost identical to the one of TIP4P/2005, adding to the similarity in the equation of state of liquid water in both models. Cooling simulations reveal that ice crystallization is fastest at the LLT and its supercritical continuation of maximum heat capacity, supporting a mechanistic relationship between the structural transformation of water to a low-density liquid (LDL) and ice formation. We find no signature of liquid-liquid criticality in the ice crystallization temperatures. ML-BOP replicates the competition between formation of LDL and ice observed in ultrafast experiments of decompression of the high-density liquid (HDL) into the region of stability of LDL. The simulations reveal that crystallization occurs prior to the coarsening of the HDL and LDL domains, obscuring the distinction between the highly metastable first-order LLT and pronounced structural fluctuations along its supercritical continuation.

Unraveling thermodynamic anomalies of water: A molecular simulation approach to probe the two-state theory with atomistic and coarse-grained water models

Thermodynamic and dynamic anomalies of water play a crucial role in supporting life on our planet. The two-state theory attributes these anomalies to a dynamic equilibrium between locally favored tetrahedral structures (LFTSs) and disordered normal liquid structures. This theory provides a straightforward, phenomenological explanation for water's unique thermodynamic and dynamic characteristics. To validate this two-state feature, it is critical to unequivocally identify these structural motifs in a dynamically fluctuating disordered liquid. In this study, we employ a recently introduced structural parameter (θavg) that characterizes the local angular order within the first coordination shell to identify these LFTSs through molecular dynamics simulations. We employ both realistic water models with a liquid-liquid critical point (LLCP) and a coarse-grained water model without an LLCP to study water's anomalies in low-pressure regions below 2 kbar. The two-state theory consistently describes water's thermodynamic anomalies in these models, both with and without an LLCP. This suggests that the anomalies predominantly result from the two-state features rather than criticality, particularly within experimentally accessible temperature-pressure regions.

Topological wetting states of microdroplets on closed-loop structured surfaces: Breakdown of the Gibbs equation at the microscale

Microdroplets are a class of soft matter that has been extensively employed for chemical, biochemical, and industrial applications. However, fabricating microdroplets with largely controllable contact-area shape and apparent contact angle, a key prerequisite for their applications, is still a challenge. Here, by engineering a type of surface with homocentric closed-loop microwalls/microchannels, we can achieve facile size, shape, and contact-angle tunability of microdroplets on the textured surfaces by design. More importantly, this class of surface topologies (with universal genus value = 1) allows us to reveal that the conventional Gibbs equation (widely used for assessing the edge effect on the apparent contact angle of macrodroplets) seems no longer applicable for water microdroplets or nanodroplets (evidenced by independent molecular dynamics simulations). Notably, for the flat surface with the intrinsic contact angle ~0°, we find that the critical contact angle on the microtextured counterparts (at edge angle 90°) can be as large as >130°, rather than 90° according to the Gibbs equation. Experiments show that the breakdown of the Gibbs equation occurs for microdroplets of different types of liquids including alcohol and hydrocarbon oils. Overall, the microtextured surface design and topological wetting states not only offer opportunities for diverse applications of microdroplets such as controllable chemical reactions and low-cost circuit fabrications but also provide testbeds for advancing the fundamental surface science of wetting beyond the Gibbs equation.

Effect of substrate mismatch, orientation, and flexibility on heterogeneous ice nucleation

Heterogeneous nucleation is the main path to ice formation on Earth. The ice nucleating ability of a certain substrate is mainly determined by both molecular interactions and the structural mismatch between the ice and the substrate lattices. We focus on the latter factor using molecular simulations of the mW model. Quantifying the effect of structural mismatch alone is challenging due to its coupling with molecular interactions. To disentangle both the factors, we use a substrate composed of water molecules in such a way that any variation on the nucleation temperature can be exclusively ascribed to the structural mismatch. We find that a 1% increase in structural mismatch leads to a decrease of ∼4 K in the nucleation temperature. We also analyze the effect of orientation of the substrate with respect to the liquid. The three main ice orientations (basal, primary prism, and secondary prism) have a similar ice nucleating ability. We finally assess the effect of lattice flexibility by comparing substrates where molecules are immobile to others where a certain freedom to fluctuate around the lattice positions is allowed. Interestingly, we find that the latter type of substrate is more efficient in nucleating ice because it can adapt its structure to that of ice.

Using machine learning with atomistic surface and local water features to predict heterogeneous ice nucleation

Heterogeneous ice nucleation (HIN) has applications in climate science, nanotechnology, and cryopreservation. Ice nucleation on the earth's surface or in the atmosphere usually occurs heterogeneously involving foreign substrates, known as ice nucleating particles (INPs). Experiments identify good INPs but lack sufficient microscopic resolution to answer the basic question: What makes a good INP? We employ molecular dynamics (MD) simulations in combination with machine learning (ML) to address this question. Often, the large amount of computational cost required to cross the nucleation barrier and observe HIN in MD simulations is a practical limitation. We use information obtained from short MD simulations of atomistic surface and water models to predict the likelihood of HIN. We consider 153 atomistic substrates with some surfaces differing in elemental composition and others only in terms of lattice parameters, surface morphology, or surface charges. A range of water features near the surface (local) are extracted from short MD simulations over a time interval (≤300 ns) where ice nucleation has not initiated. Three ML classification models, Random Forest (RF), support vector machine, and Gaussian process classification are considered, and the accuracies achieved by all three approaches lie within their statistical uncertainties. Including local water features is essential for accurate prediction. The accuracy of our best RF classification model obtained including both surface and local water features is 0.89 ± 0.05. A similar accuracy can be achieved including only local water features, suggesting that the important surface properties are largely captured by the local water features. Some important features identified by ML analysis are local icelike structures, water density and polarization profiles perpendicular to the surface, and the two-dimensional lattice match to ice. We expect that this work, with its strong focus on realistic surface models, will serve as a guide to the identification or design of substrates that can promote or discourage ice nucleation.

Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks

In this paper, we investigate the performance of different machine learning potentials (MLPs) in predicting key thermodynamic properties of water using RPBE + D3. Specifically, we scrutinize kernel-based regression and high-dimensional neural networks trained on a highly accurate dataset consisting of about 1500 structures, as well as a smaller dataset, about half the size, obtained using only on-the-fly learning. This study reveals that despite minor differences between the MLPs, their agreement on observables such as the diffusion constant and pair-correlation functions is excellent, especially for the large training dataset. Variations in the predicted density isobars, albeit somewhat larger, are also acceptable, particularly given the errors inherent to approximate density functional theory. Overall, this study emphasizes the relevance of the database over the fitting method. Finally, this study underscores the limitations of root mean square errors and the need for comprehensive testing, advocating the use of multiple MLPs for enhanced certainty, particularly when simulating complex thermodynamic properties that may not be fully captured by simpler tests.

Why neural functionals suit statistical mechanics

We describe recent progress in the statistical mechanical description of many-body systems via machine learning combined with concepts from density functional theory and many-body simulations. We argue that the neural functional theory by Sammülleret al(2023Proc. Natl Acad. Sci.120e2312484120) gives a functional representation of direct correlations and of thermodynamics that allows for thorough quality control and consistency checking of the involved methods of artificial intelligence. Addressing a prototypical system we here present a pedagogical application to hard core particle in one spatial dimension, where Percus' exact solution for the free energy functional provides an unambiguous reference. A corresponding standalone numerical tutorial that demonstrates the neural functional concepts together with the underlying fundamentals of Monte Carlo simulations, classical density functional theory, machine learning, and differential programming is available online athttps://github.com/sfalmo/NeuralDFT-Tutorial.

Abnormal condensation of water vapour at ambient temperature

The homogeneous condensation of water vapor at ambient temperature is studied using molecular dynamics simulation. We reveal that there is a droplet size at the nanoscale where water droplets can be stabilized in the condensation process. Our simulations show that the growth of water droplets is dominated by collision and coagulation between small water droplets after nucleation. This process is found to be accompanied by exceptionally fast evaporation such that droplet growth is balanced by evaporation when water droplets grow to a critical size, approximately 12.5 Å in radius, reaching a stable size distribution. The extremely high evaporation rate is attributed to the curvature dependence of surface tension. Surface tension shows a significant decrease with decreasing droplet size below 20 Å, which causes the total free energy of nanoscaled water droplets to rise after collision and coagulation. Consequently, water droplets have to shrink via fast evaporation. The curvature dependence of surface tension is related to the dielectric ordering of water molecules near the surface of water droplets. Owing to fast evaporation, secondary condensation occurs, and many small water clusters form, ultimately exhibiting a bimodal distribution of water-droplet size.

Gaussian and Non-Gaussian Solvent Density Fluctuations within Solute Cavities in a Water-like Solvent

We report a Monte Carlo simulation study of length-scale-dependent density fluctuations in cavities in the coarse-grained mW representation of water at ambient conditions. Specifically, we use a combination of test particle insertion and umbrella sampling techniques to examine the full range of water occupation states in spherical cavities up to 6.3 Å radius in water. As has previously been observed, water density fluctuations are found to be effectively Gaussian in nature for atomic-scale cavities, but as the cavities get larger, they exhibit a non-Gaussian "fat-tail" distribution for lower occupancy states. We introduce a new statistical thermodynamic approach to analyze non-Gaussian fluctuations based on the radial distribution of waters about cavities with varying numbers of waters within its boundaries. It is shown that the onset of these non-Gaussian fluctuations is a result of the formation of a bubble within the cavity as it is emptied, which is accompanied by the adsorption of waters onto its interior surface. We revisit a theoretical framework we previously introduced to describe Gaussian fluctuations within cavities to incorporate bubble formation by including surface tension contributions. This modified theory accurately describes density fluctuations within both atomic and meso-scale cavities. Moreover, the theory predicts the transition from Gaussian to non-Gaussian fluctuations at a specific cavity occupancy, in excellent agreement with simulation observations.

Representability and Dynamical Consistency in Coarse-Grained Models

We address the challenge of representativity and dynamical consistency when unbonded fine-grained particles are collected together into coarse-grained particles. We implement a hybrid procedure for identifying and tracking the underlying fine-grained particles─e.g., atoms or molecules─by exchanging them between the coarse-grained particles periodically at a characteristic time. The exchange involves a back-mapping of the coarse-grained particles into fine-grained particles and a subsequent reassignment to coarse-grained particles conserving total mass and momentum. We find that an appropriate choice of the characteristic exchange time can lead to the correct effective diffusion rate of the fine-grained particles when simulated in hybrid coarse-grained dynamics. In the compressed (supercritical) fluid regime, without the exchange term, fine-grained particles remain associated with a given coarse-grained particle, leading to substantially lower diffusion rates than seen in all-atom molecular dynamics of the fine-grained particles. Thus, this work confirms the need for addressing the representativity of fine-grained particles within coarse-grained particles and offers a simple exchange mechanism so as to retain dynamical consistency between the fine- and coarse-grained scales.

The limit of macroscopic homogeneous ice nucleation at the nanoscale

Nucleation in small volumes of water has garnered renewed interest due to the relevance of pore condensation and freezing under conditions of low partial pressures of water, such as in the upper troposphere. Molecular simulations can in principle provide insight on this process at the molecular scale that is challenging to achieve experimentally. However, there are discrepancies in the literature as to whether the rate in confined systems is enhanced or suppressed relative to bulk water at the same temperature and pressure. In this study, we investigate the extent to which the size of the critical nucleus and the rate at which it grows in thin films of water are affected by the thickness of the film. Our results suggest that nucleation remains bulk-like in films that are barely large enough accommodate a critical nucleus. This conclusion seems robust to the presence of physical confining boundaries. We also discuss the difficulties in unambiguously determining homogeneous nucleation rates in nanoscale systems, owing to the challenges in defining the volume. Our results suggest any impact on a film's thickness on the rate is largely inconsequential for present day experiments.

Concluding remarks for Faraday Discussion on Water at Interfaces

Water at interfaces is a fascinating and multifaceted topic that has garnered significant attention in various scientific fields due to its relevance and implications. This Faraday Discussion explored the complexity of water at different interfaces. Many of the reports highlight the need for a molecular-level understanding. The Discussion was lively and constructive. In these summarizing remarks, I do not aim to be complete, but will rather try to sketch the status of the field, highlight the progress that we as a community have made, and present eclectic examples of where more work needs to be done.

Evidence of Spontaneous Formation of Two-Dimensional Amorphous Clathrates on Superhydrophilic Surfaces

Clathrate hydrates reserved in the seabed are often dispersed in the pores of coarse-grained sediments; hence, their formation typically occurs under nanoconfinement. Herein, we show the first molecular dynamics (MD) simulation evidence of the spontaneous formation of two-dimensional (2D) clathrate hydrates on crystal surfaces without conventional nanoconfinement. The kinetic process of 2D clathrate formation is illustrated via simulated single-molecule deposition. 2D amorphous patterns are observed on various superhydrophilic face-centered cubic surfaces. Notably, the formation of 2D amorphous clathrate can occur over a wide range of temperatures, even at room temperature. The strong water-surface interaction, the characteristic properties of guest-gas molecules, and the underlying surface structure dictate the formation of 2D amorphous clathrates. Semiquantitative phase diagrams of 2D clathrates are constructed where representative patterns of 2D clathrates for characteristic gas molecules on prototypical Pd(111) and Pt(111) surfaces are confirmed by independent MD simulations. A tunable pattern of 2D amorphous clathrates is demonstrated by changing the lattice strain of the underlying substrate. Moreover, ab initio MD simulations confirm the stability of 2D amorphous clathrate. The underlining physical mechanism for 2D clathrate formation on superhydrophilic surfaces is elucidated, which offers deeper insight into the crucial role of water-surface interaction.

Multiscale Molecular Dynamics Simulations of Ice-Binding Proteins

Ice-binding proteins (IBPs) are a diverse class of proteins that are essential for the survival of organisms in cold conditions. IBPs are diverse in their function and can prevent or promote ice growth and selectively bind to specific crystallographic planes of the growing ice lattice. Moreover, IBPs are widely utilized to modulate ice crystal growth and recrystallization in the food industry and as cryoprotectants to preserve biological matter. A key unresolved aspect of the mode of action is how the ice-binding sites of these proteins distinguish between ice and water and interact with multiple crystal facets of the ice. The use of molecular dynamics (MD) simulation allows us to thoroughly investigate the binding mechanism and energetics of ice-binding proteins, to complement and expand on the mechanistic understandings gained from experiments. In this chapter, we describe a series of molecular dynamics simulation methodologies to investigate the mechanism of action of ice-binding proteins. Specifically, we provide detailed instructions to set up MD simulations to study the binding and interaction of ice-binding proteins using atomistic and coarse-grained simulations.

Neural functional theory for inhomogeneous fluids: Fundamentals and applications

We present a hybrid scheme based on classical density functional theory and machine learning for determining the equilibrium structure and thermodynamics of inhomogeneous fluids. The exact functional map from the density profile to the one-body direct correlation function is represented locally by a deep neural network. We substantiate the general framework for the hard sphere fluid and use grand canonical Monte Carlo simulation data of systems in randomized external environments during training and as reference. Functional calculus is implemented on the basis of the neural network to access higher-order correlation functions via automatic differentiation and the free energy via functional line integration. Thermal Noether sum rules are validated explicitly. We demonstrate the use of the neural functional in the self-consistent calculation of density profiles. The results outperform those from state-of-the-art fundamental measure density functional theory. The low cost of solving an associated Euler-Lagrange equation allows to bridge the gap from the system size of the original training data to macroscopic predictions upon maintaining near-simulation microscopic precision. These results establish the machine learning of functionals as an effective tool in the multiscale description of soft matter.

Cavity formation at metal-water interfaces

The free energy cost of forming a cavity in a solvent is a fundamental concept in rationalizing the solvation of molecules and ions. A detailed understanding of the factors governing cavity formation in bulk solutions has inter alia enabled the formulation of models that account for this contribution in coarse-grained implicit solvation methods. Here, we employ classical molecular dynamics simulations and multistate Bennett acceptance ratio free energy sampling to systematically study cavity formation at a wide range of metal-water interfaces. We demonstrate that the obtained size- and position-dependence of cavitation energies can be fully rationalized by a geometric Gibbs model, which considers that the creation of the metal-cavity interface necessarily involves the removal of interfacial solvent. This so-called competitive adsorption effect introduces a substrate dependence to the interfacial cavity formation energy that is missed in existing bulk cavitation models. Using expressions from scaled particle theory, this substrate dependence is quantitatively reproduced by the Gibbs model through simple linear relations with the adsorption energy of a single water molecule. Besides providing a better general understanding of interfacial solvation, this paves the way for the derivation and efficient parametrization of more accurate interface-aware implicit solvation models needed for reliable high-throughput calculations toward improved electrocatalysts.

Atomic-Level Insights into Hollow Silica-Based Materials for Drug Delivery: Effects of Wettability and Porosity

Experimental evidence has demonstrated that the drug carrier capacity can be significantly enhanced through the use of hollow silica particles. Nevertheless, the effects of varying functional drug carrier surfaces and porous structures remain ambiguous. This study employs molecular dynamics simulations to examine the effects of varying the surface wettability, pore size, and flow velocity on the transfer process. The different levels of wettability of the silica surface with the coarse-grained water model is illustrated by adjusted interaction parameters. The effect of wettability is investigated. With weak interactions, the flow molecules form a nanodroplet to transfer through the porous structure. A strong interaction will lead to molecules flowing as a liquid film to transfer through the structure. Interestingly, the "contradiction effect" is observed when the flow molecules fail to penetrate the porous structure with weak interactions, during which surface tension dominates their flow behavior. Moreover, different porous structures are considered. The flow behaviors are divided into three processes: (1) fast flowing, (2) transient point, and (3) penetration flowing. Furthermore, the concept of surface molecules is defined to quantitatively measure the effect of porosity. A recommended contact angle is proposed. The results will pave the way for more carrier structures in medical engineering.

Neural Network Water Model Based on the MB-Pol Many-Body Potential

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

Rolling and Sliding Modes of Nanodroplet Spreading: Molecular Simulations and a Continuum Approach

Molecular simulations discover a new mode of dynamic wetting that manifests itself in the very earliest stages of spreading, after a droplet contacts a solid. The observed mode is a "rolling" type of motion, characterized by a contact angle lower than the classically assumed value of 180°, and precedes the conventional "sliding" mode of spreading. This motivates the development of a novel continuum framework that captures all modes of motion, allows the dominant physical mechanisms to be understood, and permits the study of larger droplets.

Thermodynamic anomalies, polyamorphism and all that

The appearance and evolution of thermodynamics anomalies, and related properties, are studied for two classes of system, modelling those dominated by covalent and ionic interactions, respectively. Such anomalies are most familiar in the density but are also present in other thermodynamics variables such as the compressibility and heat capacity. By systematically varying key model parameters the emergence and evolution of these anomalies can be tracked across the phase space. The interaction of the anomalies can often be rationalized by thermodynamics 'rules'. The emergence of these anomalies may also be correlated with the appearance of polyamorphism, the existence of multiple amorphous states which differ in density and entropy. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.

Does freezing induce self-assembly of polymers? A molecular dynamics study

This work investigates the freezing-induced self-assembly (FISA) of polyvinyl alcohol (PVA) and PVA-like polymers using molecular dynamics simulations. In particular, the effect of the degree of supercooling, degree of polymerization, polymer type, and initial local concentration on the FISA was studied. It was found that the preeminent factor responsible for FISA is not the diffusion of the polymers away from the nucleating ice front, but the increase in the polymer's local concentration upon freezing of the solvent (water). At a higher degree of supercooling, the polymers are engulfed by the growing ice front, impeding their diffusion into the supercooled solution and finally inhibiting their self-assembly. Conversely, at a relatively lower degree of supercooling, the rate of diffusion of the polymers into the supercooled solution is higher, which increases their local concentration and results in FISA. FISA was also observed to depend on the polymer-solvent interactions. Strongly favorable solute-solvent interactions hinder the self-assembly, whereas unfavorable solute-solvent interactions promote the self-assembly. The polymer and aggregate morphology were investigated using the radius of gyration, end-to-end distance, and asphericity analysis. This study brings molecular insights into the quintessential factors governing self-assembly via freezing of the solvent, which is a novel self-assembly technique especially suitable for biomedical applications.

Molecular dynamics simulations of the dielectric constants of salt-free and salt-doped polar solvents

We develop a Stockmayer fluid model that accounts for the dielectric responses of polar solvents (water, MeOH, EtOH, acetone, 1-propanol, DMSO, and DMF) and NaCl solutions. These solvent molecules are represented by Lennard-Jones (LJ) spheres with permanent dipole moments and the ions by charged LJ spheres. The simulated dielectric constants of these liquids are comparable to experimental values, including the substantial decrease in the dielectric constant of water upon the addition of NaCl. Moreover, the simulations predict an increase in the dielectric constant when considering the influence of ion translations in addition to the orientation of permanent dipoles.

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations

Water adsorption and dissociation processes on pristine low-index TiO2 interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO2 surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO2 surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces.

What Controls the Hole Formation of Nanodroplets: Hydrodynamic or Thermodynamic Instability?

Using molecular dynamics simulations, we investigate the air hole formation of water nanodroplets impacting hydrophilic to hydrophobic surfaces in the range of static contact angles from 30° to 140° with different initial surface temperatures ranging from 300 to 1000 K. We show that the hole dynamics of nanodroplets are different from those observed in millimeter-sized droplets. The hole formation can be observed on smooth surfaces for nanodroplets; however, it only occurs on nonsmooth surfaces for millimeter-sized droplets. We clarify that the hole formation of nanodroplets is triggered by a nucleated vapor bubble due to thermodynamic instability, whereas it is initiated by air bubble entrapment during impact due to hydrodynamic instability for millimeter-sized droplets. The hole formation of nanodroplets relies heavily on the surface temperature and surface wettability, because the nucleated vapor bubble more easily occurs and grows on the surface with high initial temperatures and hydrophobic surfaces. Based on the thermal stability analysis, a criterion is developed to predict the hole formation of nanodroplets, which verifies the dependence of hole formation on the surface temperature and wettability. Furthermore, we show that the ring-bouncing of nanodroplets is triggered by the nucleated vapor bubble. We clarify the reasons for the reduced contact time of nanodroplets caused by the ring-bouncing.

Large-Scale Nonequilibrium Molecular Studies of Thermal Hydrate Dissociation

The energy content of methane hydrate reservoirs (MHRs) is at least twice that of conventional fossil fuels. So, there is considerable interest in their commercial development by heating, among other dissociation mechanisms. However, a few researchers have highlighted the potentially uncontrollable release of methane from MHRs, which could occur because of global warming. Therefore, it is crucial to understand the kinetics of thermal hydrate dissociation to safely develop these resources and prevent the release of this greenhouse gas into the environment. Although there have been several molecular studies of thermal dissociation, most of these use small simulation domains that cannot capture the transient nature of the process. To address this limitation, we performed coarse-grained molecular dynamics (CGMD) simulations on a significantly larger domain with a hundred times more hydrate unit cells than those used in previous studies. We monitored the kinetics of dissociation using an image-processing algorithm and observed the dynamics of the process while maintaining a thermal gradient at the dissociation front. For the first time, we report the formation of an unstable secondary dissociation path that triggers gas bubbles within the solid hydrate. The kinetics of thermal dissociation appears to occur in three stages. In the first stage, the energy of the system increases until it exceeds the activation energy, and dissociation is initiated. Consistent dissociation occurs in the second stage, whereas the third stage involves the dissociation of the remaining hydrates across a nonplanar and heterogeneous interface.

Local measures of fluctuations in inhomogeneous liquids: statistical mechanics and illustrative applications

We show in detail how three one-body fluctuation profiles, namely the local compressibility, the local thermal susceptibility, and the reduced density, can be obtained from a statistical mechanical many-body description of classical particle-based systems. We present several different and equivalent routes to the definition of each fluctuation profile, facilitating their explicit numerical calculation in inhomogeneous equilibrium systems. This underlying framework is used for the derivation of further properties such as hard wall contact theorems and novel types of inhomogeneous one-body Ornstein-Zernike equations. The practical accessibility of all three fluctuation profiles is exemplified by grand canonical Monte Carlo simulations that we present for hard sphere, Gaussian core and Lennard-Jones fluids in confinement.

Noether-Constrained Correlations in Equilibrium Liquids

Liquid structure carries deep imprints of an inherent thermal invariance against a spatial transformation of the underlying classical many-body Hamiltonian. At first order in the transformation field Noether's theorem yields the local force balance. Three distinct two-body correlation functions emerge at second order, namely the standard two-body density, the localized force-force correlation function, and the localized force gradient. An exact Noether sum rule interrelates these correlators. Simulations of Lennard-Jones, Yukawa, soft-sphere dipolar, Stockmayer, Gay-Berne and Weeks-Chandler-Andersen liquids, of monatomic water and of a colloidal gel former demonstrate the fundamental role in the characterization of spatial structure.

Optimum substrate stiffness in coalescence-induced droplet jumping

When droplets are brought into contact and coalesced on a superhydrophobic surface, the kinetic energy converted from the surface energy enables the merged droplet to jump. Current studies mainly focus on the microstructure of surfaces and the properties of droplets that influence the jumping dynamics. Here, by means of molecular dynamics, we investigate the coalescence-induced jumping of nanodroplets on soft substrates. The optimum stiffness of the substrate is suggested and the mechanism involved is demonstrated through the analysis of the interactions between the droplets and the substrates. The momentum of the droplet is evaluated by integrating the forces from the substrate. The optimum stiffness for jumping velocity is provided by the competition between the impact and the adhesion from the substrate during the process, which are both closely related to the stiffness. The results may inspire fundamental research and applications in a broad scope.

A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

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.

Solubility of carbon dioxide in water: Some useful results for hydrate nucleation

In this paper, the solubility of carbon dioxide (CO2) in water along the isobar of 400 bar is determined by computer simulations using the well-known TIP4P/Ice force field for water and the TraPPE model for CO2. In particular, the solubility of CO2 in water when in contact with the CO2 liquid phase and the solubility of CO2 in water when in contact with the hydrate have been determined. The solubility of CO2 in a liquid-liquid system decreases as the temperature increases. The solubility of CO2 in a hydrate-liquid system increases with temperature. The two curves intersect at a certain temperature that determines the dissociation temperature of the hydrate at 400 bar (T3). We compare the predictions with T3 obtained using the direct coexistence technique in a previous work. The results of both methods agree, and we suggest 290(2) K as the value of T3 for this system using the same cutoff distance for dispersive interactions. We also propose a novel and alternative route to evaluate the change in chemical potential for the formation of hydrates along the isobar. The new approach is based on the use of the solubility curve of CO2 when the aqueous solution is in contact with the hydrate phase. It considers rigorously the non-ideality of the aqueous solution of CO2, providing reliable values for the driving force for nucleation of hydrates in good agreement with other thermodynamic routes used. It is shown that the driving force for hydrate nucleation at 400 bar is larger for the methane hydrate than for the carbon dioxide hydrate when compared at the same supercooling. We have also analyzed and discussed the effect of the cutoff distance of dispersive interactions and the occupancy of CO2 on the driving force for nucleation of the hydrate.

Are Neural Network Potentials Trained on Liquid States Transferable to Crystal Nucleation? A Test on Ice Nucleation in the mW Water Model

Neural network potentials (NNPs) are increasingly being used to study processes that happen on long time scales. A typical example is crystal nucleation, which rate is controlled by the occurrence of a rare fluctuation, i.e., the appearance of the critical nucleus. Because the properties of this nucleus are far from those of the bulk crystal, it is yet unclear whether NN potentials trained on equilibrium liquid states can accurately describe nucleation processes. So far, nucleation studies on NNPs have been limited to ab initio models whose nucleation properties are unknown, preventing an accurate comparison. Here we train a NN potential on the mW model of water─a classical three-body potential whose nucleation time scale is accessible in standard simulations. We show that a NNP trained only on a small number of liquid state points can reproduce with great accuracy the nucleation rates and free energy barriers of the original model, computed from both spontaneous and biased trajectories, strongly supporting the use of NNPs to study nucleation events.

RSeeds: Rigid Seeding Method for Studying Heterogeneous Crystal Nucleation

Heterogeneous nucleation is the dominant form of liquid-to-solid transition in nature. Although molecular simulations are most uniquely suited to studying nucleation, the waiting time to observe even a single nucleation event can easily exceed the current computational capabilities. Therefore, there exists an imminent need for methods that enable computationally fast and feasible studies of heterogeneous nucleation. Seeding is a technique that has proven to be successful at dramatically expanding the range of computationally accessible nucleation rates in simulation studies of homogeneous crystal nucleation. In this article, we introduce a new seeding method for heterogeneous nucleation called Rigid Seeding (RSeeds). Crystalline seeds are treated as pseudorigid bodies and simulated on a surface with metastable liquid above its melting temperature. This allows the seeds to adapt to the surface and identify favorable seed-surface configurations, which is necessary for reliable predictions of crystal polymorphs that form and the corresponding heterogeneous nucleation rates. We demonstrate and validate RSeeds for heterogeneous ice nucleation on a flexible self-assembled monolayer surface, a mineral surface based on kaolinite, and two model surfaces. RSeeds predicts the correct ice polymorph, exposed crystal plane, and rotation on the surface. RSeeds is semiquantitative and can be used to estimate the critical nucleus size and nucleation rate when combined with classical nucleation theory. We demonstrate that RSeeds can be used to evaluate nucleation rates spanning many orders of magnitude.

Tracking cubic ice at molecular resolution

Ice is present everywhere on Earth and has an essential role in several areas, such as cloud physics, climate change and cryopreservation. The role of ice is determined by its formation behaviour and associated structure. However, these are not fully understood1. In particular, there is a long-standing debate about whether water can freeze to form cubic ice-a currently undescribed phase in the phase space of ordinary hexagonal ice2-6. The mainstream view inferred from a collection of laboratory data attributes this divergence to the inability to discern cubic ice from stacking-disordered ice-a mixture of cubic and hexagonal sequences7-11. Using cryogenic transmission electron microscopy combined with low-dose imaging, we show here the preferential nucleation of cubic ice at low-temperature interfaces, resulting in two types of separate crystallization of cubic ice and hexagonal ice from water vapour deposition at 102 K. Moreover, we identify a series of cubic-ice defects, including two types of stacking disorder, revealing the structure evolution dynamics supported by molecular dynamics simulations. The realization of direct, real-space imaging of ice formation and its dynamic behaviour at the molecular level provides an opportunity for ice research at the molecular level using transmission electron microscopy, which may be extended to other hydrogen-bonding crystals.