Pre-critical fluctuations and what they disclose about heterogeneous crystal nucleation

Icing and Adhesion Behaviors on Surfaces with Varied Lattice Constants

Investigating droplet wetting and icing behavior is crucial for comprehending the principles of surface icing and the design of anti-icing surfaces. In this study, we present the evidence from molecular dynamics (MD) simulations that reveal a hitherto unreported behavior of droplet wetting and icing adhesion on surfaces with lattice constants from 2.7 to 4.5 Å. Here, we observe that the contact angles (CA) of droplets on a face-centered cubic (FCC) lattice surface consistently correlate positively with the lattice constant. Further examination of droplet behavior on an idealized crystal surface reveals that hydrophilic surfaces (e.g., CA = 85°) inhibit freezing more effectively than hydrophobic surfaces (e.g., CA = 97°). This finding contradicts the conventional explanation that hydrophobic surfaces reduce heterogeneous nucleation, thereby delaying icing. This study introduces a mechanistic explanation for the promotion of water icing by hydrophobic surfaces and offers a novel design concept for the development of anti-ice surfaces in future applications.

Hydrogen-bond linking is crucial for growing ice VII embryos

We use molecular dynamics simulations to examine the homogeneous nucleation of ice VII from metastable liquid water. An unsupervised machine learning classification identifies two distinct local structures composing Ice VII nuclei. The seeding method, combined with the classical nucleation theory (CNT), predicts the solid-liquid interfacial free energy, consistent with the value from the mold integration method. Meanwhile, the nucleation rates estimated from the CNT framework and brute force spontaneous nucleations are inconsistent, and we discuss the reasons for this discrepancy. Structural and dynamical heterogeneities suggest that the potential birthplace for an ice VII embryo is relatively ordered, although not necessarily relatively immobile. Moreover, we demonstrate that without the formation of hydrogen-bond links, ice VII embryos do not grow.

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.

Controlling crystallization: what liquid structure and dynamics reveal about crystal nucleation mechanisms

Over recent years, molecular simulations have provided invaluable insights into the microscopic processes governing the initial stages of crystal nucleation and growth. A key aspect that has been observed in many different systems is the formation of precursors in the supercooled liquid that precedes the emergence of crystalline nuclei. The structural and dynamical properties of these precursors determine to a large extent the nucleation probability as well as the formation of specific polymorphs. This novel microscopic view on nucleation mechanisms has further implications for our understanding of the nucleating ability and polymorph selectivity of nucleating agents, as these appear to be strongly linked to their ability in modifying structural and dynamical characteristics of the supercooled liquid, namely liquid heterogeneity. In this perspective, we highlight recent progress in exploring the connection between liquid heterogeneity and crystallization, including the effects of templates, and the potential impact for controlling crystallization processes. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

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.

Toward Precision Deposition of Conductive Charge-Transfer Complex Crystals Using Nanoelectrochemistry

The lack of understanding for precise synthesis and assembly of nano-entities remains a major challenge for nanofabrication. Electrocrystallization of a charge-transfer complex (CTC), tetrathiafulvalene bromide (TTF)Br, is studied on micro/nanoelectrodes for precision deposition of functional materials. The study reveals new insights into the entire CTC electrocrystallization process from the initial nanocluster nucleation to the final elongated crystals with hollow ends grown from the working electrode to the neighboring receiving electrode. On microelectrodes, the number of nucleation sites is reduced to one by lowering the applied overpotential or precursor concentration. Certain current-time transients exhibit significant induction periods prior to stable nucleus growth. The induction regime contains small fluctuating current spikes consistent with stochastic formation of precritical nanoclusters with lifetimes of 0.1-30 s and sizes of 20-160 nm. Electrochemical analyses further reveal rate, size distribution, and formation/dissipation dynamics of the nanoclusters. Crystal growth of (TTF)Br is further studied on triangular nanoelectrode patterns with thickness of 5-500 nm, which shows a mass-transfer-controlled process applicable for precision deposition of functional (TTF)Br crystals. This study, for the first time, establishes CTC nanoelectrochemistry as a platform technology for precise deposition of conductive crystal assemblies spanning the source and drain electrode for sensing applications.

Nucleation kinetics model for primary crystallization in Al-Y-Fe metallic glass

The high density of aluminum nanocrystals (>10²¹ m^-3) that develop during the primary crystallization in Al-based metallic glasses indicates a high nucleation rate (∼10¹⁸ m^-3 s^-1). Several studies have been advanced to account for the primary crystallization behavior, but none have been developed to completely describe the reaction kinetics. Recently, structural analysis by fluctuation electron microscopy has demonstrated the presence of the Al-like medium range order (MRO) regions as a spatial heterogeneity in as-spun Al₈₈Y₇Fe₅ metallic glass that is representative for the class of Al-based amorphous alloys that develop Al nanocrystals during primary crystallization. From the structural characterization, an MRO seeded nucleation configuration is established, whereby the Al nanocrystals are catalyzed by the MRO core to decrease the nucleation barrier. The MRO seeded nucleation model and the kinetic data from the delay time (τ) measurement provide a full accounting of the evolution of the Al nanocrystal density (N_v) during the primary crystallization under isothermal annealing treatments. Moreover, the calculated values of the steady state nucleation rates (J_ss) predicted by the nucleation model agree with the experimental results. Moreover, the model satisfies constraints on the structural, thermodynamic, and kinetic parameters, such as the critical nucleus size, the interface energy, and the volume-free energy driving force that are essential for a fully self-consistent nucleation kinetics analysis. The nucleation kinetics model can be applied more broadly to materials that are characterized by the presence of spatial heterogeneities.

Free energy calculations and unbiased molecular dynamics targeting the liquid-liquid transition in water no man's land

The existence of a first-order phase transition between a low-density liquid (LDL) and a high-density liquid (HDL) form of supercooled water has been a central and highly debated issue of physics and chemistry for the last three decades. We present a computational study that allows us to determine the free-energy landscapes of supercooled water over a wide range of pressure and temperature conditions using the TIP4P/2005 force field. Our approach combines topology-based structural transformation coordinates, state-of-the-art free-energy calculation methods, and extensive unbiased molecular dynamics. All our diverse simulations cannot detect any barrier within the investigated timescales and system size, for a discontinuous transition between the LDL and HDL forms throughout the so-called "no man's land," until the onset of the solid, non-diffusive amorphous forms.

Critical comparison of general-purpose collective variables for crystal nucleation

The nucleation of crystals is a prominent phenomenon in science and technology that still lacks a full atomic-scale understanding. Much work has been devoted to identifying order parameters able to track the process, from the inception of early nuclei to their maturing to critical size until growth of an extended crystal. We critically assess and compare two powerful distance-based collective variables, an effective entropy derived from liquid state theory and the path variable based on permutation invariant vectors using the Kob-Andersen binary mixture and a combination of enhanced-sampling techniques. Our findings reveal a comparable ability to drive nucleation when a bias potential is applied, and comparable free-energy barriers and structural features. Yet, we also found an imperfect correlation with the committor probability on the barrier top which was bypassed by changing the order parameter definition.

Ab initio molecular dynamics free energy study of enhanced copper (II) dimerization on mineral surfaces

Understanding the adsorption of isolated metal cations from water on to mineral surfaces is critical for toxic waste retention and cleanup in the environment. Heterogeneous nucleation of metal oxyhydroxides and other minerals on material surfaces is key to crystal growth and dissolution. The link connecting these two areas, namely cation dimerization and polymerization, is far less understood. In this work we apply ab initio molecular dynamics calculations to examine the coordination structure of hydroxide-bridged Cu(II) dimers, and the free energy changes associated with Cu(II) dimerization on silica surfaces. The dimer dissociation pathway involves sequential breaking of two Cu2+-OH- bonds, yielding three local minima in the free energy profiles associated with 0-2 OH- bridges between the metal cations, and requires the design of a (to our knowledge) novel reaction coordinate for the simulations. Cu(II) adsorbed on silica surfaces are found to exhibit stronger tendency towards dimerization than when residing in water. Cluster-plus-implicit-solvent methods yield incorrect trends if OH- hydration is not correctly depicted. The predicted free energy landscapes are consistent with fast equilibrium times (seconds) among adsorbed structures, and favor Cu2+ dimer formation on silica surfaces over monomer adsorption.

The role of structural order in heterogeneous ice nucleation

The freezing of water into ice is a key process that is still not fully understood. It generally requires an impurity of some description to initiate the heterogeneous nucleation of the ice crystals. The molecular structure, as well as the extent of structural order within the impurity in question, both play an essential role in determining its effectiveness. However, disentangling these two contributions is a challenge for both experiments and simulations. In this work, we have systematically investigated the ice-nucleating ability of the very same compound, cholesterol, from the crystalline (and thus ordered) form to disordered self-assembled monolayers. Leveraging a combination of experiments and simulations, we identify a “sweet spot” in terms of the surface coverage of the monolayers, whereby cholesterol maximises its ability to nucleate ice (which remains inferior to that of crystalline cholesterol) by enhancing the structural order of the interfacial water molecules. These findings have practical implications for the rational design of synthetic ice-nucleating agents.

The freezing of water into ice is still not fully understood. Here, we investigate the role of structural disorder within the biologically relevant impurities that facilitate this fundamental phase transition.

Impurity diffusion in magic-size icosahedral clusters

Atomic diffusion is at the basis of chemical ordering transformations in nanoalloys. Understanding the diffusion mechanisms at the atomic level is therefore a key issue in the study of the thermodynamic behavior of these systems and, in particular, of their evolution from out-of-equilibrium chemical ordering types often obtained in the experiments. Here, the diffusion is studied in the case of a single-atom impurity of Ag or Au moving within otherwise pure magic-size icosahedral clusters of Cu or Co by means of two different computational techniques, i.e., molecular dynamics and metadynamics. Our simulations reveal unexpected diffusion pathways, in which the displacement of the impurity is coupled with the creation of vacancies in the central part of the cluster. We show that the observed mechanism is quite different from the vacancy-mediated diffusion processes identified so far, and we demonstrate that it can be related to the presence of non-homogeneous compressive stress in the inner part of the icosahedral structure.

Investigations of structural and dynamical mechanisms of ice formation regulated by graphene oxide nanosheets

Recent research indicates that graphene oxide (GO) nanosheets can be used to regulate ice formation by controlling critical ice nucleus growth in water at supercooling temperatures. In addition, the study of ice formation mechanisms regulated by GO nanosheets, a good model system for antifreeze proteins (AFPs), will shed light on how AFPs regulate ice formation in nature. In this work, time-resolved small-angle x-ray scattering (TR-SAXS) and quasi-elastic neutron scattering (QENS) experiments were carried out to investigate the structural and dynamical mechanisms of ice formation regulated by GO nanosheets. Strikingly, a transient intermediate state was observed in TR-SAXS experiments that only exists in the aqueous dispersions with a larger GO size (11 nm). This serves as evidence that the size of GO is critical for regulating ice formation. Elastic neutron scattering results indicate that ice is formed in all samples and thermal hysteresis occurs in GO aqueous dispersions in both H₂O and D₂O. The structural and dynamics information about water molecules in GO, extracted from QENS, reveals different dynamical behaviors of water molecules in GO aqueous dispersions when approaching the ice formation temperature.

Temperature-dependent kinetic pathways of heterogeneous ice nucleation competing between classical and non-classical nucleation

Ice nucleation on the surface plays a vital role in diverse areas, ranging from physics and cryobiology to atmospheric science. Compared to ice nucleation in the bulk, the water-surface interactions present in heterogeneous ice nucleation complicate the nucleation process, making heterogeneous ice nucleation less comprehended, especially the relationship between the kinetics and the structures of the critical ice nucleus. Here we combine Markov State Models and transition path theory to elucidate the ensemble pathways of heterogeneous ice nucleation. Our Markov State Models reveal that the classical one-step and non-classical two-step nucleation pathways can surprisingly co-exist with comparable fluxes at T = 230 K. Interestingly, we find that the disordered mixing of rhombic and hexagonal ice leads to a favorable configurational entropy that stabilizes the critical nucleus, facilitating the non-classical pathway. In contrast, the favorable energetics promotes the formation of hexagonal ice, resulting in the classical pathway. Furthermore, we discover that, at elevated temperatures, the nucleation process prefers to proceed via the classical pathway, as opposed to the non-classical pathway, since the potential energy contributions override the configurational entropy compensation. This study provides insights into the mechanisms of heterogeneous ice nucleation and sheds light on the rational designs to control crystallization processes.

Is Ice Nucleation by Organic Crystals Nonclassical? An Assessment of the Monolayer Hypothesis of Ice Nucleation

Potent ice nucleating organic crystals display an increase in nucleation efficiency with pressure and memory effect after pressurization that set them apart from inorganic nucleants. These characteristics were proposed to arise from an ordered water monolayer at the organic-water interface. It was interpreted that ordering of the monolayer is the limiting step for ice nucleation on organic crystals, rendering their mechanism of nucleation nonclassical. Despite the importance of organics in atmospheric ice nucleation, that explanation has never been investigated. Here we elucidate the structure of interfacial water and its role in ice nucleation at ambient pressure on phloroglucinol dihydrate, the paradigmatic example of outstanding ice nucleating organic crystal, using molecular simulations. The simulations confirm the existence of an interfacial monolayer that orders on cooling and becomes fully ordered upon ice formation. The monolayer does not resemble any ice face but seamlessly connects the distinct hydrogen-bonding orders of ice and the organic surface. Although large ordered patches develop in the monolayer before ice nucleates, we find that the critical step is the formation of the ice crystallite, indicating that the mechanism is classical. We predict that the fully ordered, crystalline monolayer nucleates ice above -2 °C and could be responsible for the exceptional ice nucleation by the organic crystal at high pressures. The lifetime of the fully ordered monolayer around 0 °C, however, is too short to account for the memory effect reported in the experiments. The latter could arise from an increase in the melting temperature of ice confined by strongly ice-binding surfaces.

Routes to cubic ice through heterogeneous nucleation

The freezing of water into ice is one of the most important processes in the physical sciences. However, it is still not understood at the molecular level. In particular, the crystallization of cubic ice ([Formula: see text])-rather than the traditional hexagonal polytype ([Formula: see text])-has become an increasingly debated topic. Although evidence for [Formula: see text] is thought to date back almost 400 y, it is only in the last year that pure [Formula: see text] has been made in the laboratory, and these processes involved high-pressure ice phases. Since this demonstrates that pure [Formula: see text] can form, the question naturally arises if [Formula: see text] can be made from liquid water. With this in mind, we have performed a high-throughput computational screening study involving molecular dynamics simulations of nucleation on over 1,100 model substrates. From these simulations, we find that 1) many different substrates can promote the formation of pristine [Formula: see text]; 2) [Formula: see text] can be selectively nucleated for even the mildest supercooling; 3) the water contact layer's resemblance to a face of ice is the key factor determining the polytype selectivity and nucleation temperature, independent of which polytype is promoted; and 4) substrate lattice match to ice is not indicative of the polytype obtained. Through this study, we have deepened understanding of the interplay of heterogeneous nucleation and ice I polytypism and suggest routes to [Formula: see text] More broadly, the substrate design methodology presented here combined with the insight gained can be used to understand and control polymorphism and stacking disorder in materials in general.

Multivalent Surface Cations Enhance Heterogeneous Freezing of Water on Muscovite Mica

Heterogeneous ice nucleation is a crucial phenomenon in various fields of fundamental and applied science. We investigate the effect of surface cations on freezing of water on muscovite mica. Mica is unique in that the exposed ion on its surface can be readily and easily exchanged without affecting other properties such as surface roughness. We investigate freezing on natural (K⁺) mica and mica in which we have exchanged K⁺ for Al³⁺, Mg²⁺, Ca²⁺, and Sr²⁺. We find that liquid water freezes at higher temperatures when ions of higher valency are present on the surface, thus exposing more of the underlying silica layer. Our data also show that the size of the ion affects the characteristic freezing temperature. Using molecular dynamics simulations, we investigate the effects that the ion valency and exposed silica layer have on the behavior of water on the surface. The results indicate that multivalent cations enhance the probability of forming large clusters of hydrogen bonded water molecules that are anchored by the hydration shells of the cations. These clusters also have a large fraction of free water that can reorient to take ice-like configurations, which are promoted by the regions on mica devoid of the ions. Thus, these clusters could serve as seedbeds for ice nuclei. The combined experimental and simulation studies shed new light on the influence of surface ions on heterogeneous ice nucleation.

Use of Ion Exchange To Regulate the Heterogeneous Ice Nucleation Efficiency of Mica

Heterogeneous ice nucleation (HIN) triggered by mineral surfaces typically exposed to various ions can have a significant impact on the regional atmosphere and climate. However, the dependence of HIN on the nature of the mineral surface ions is still largely unexplored due to the complexity of mineral surfaces. Because K⁺ on the atomically flat (001) surface of mica can be readily replaced by different cations through ion exchange, muscovite mica was selected; its simple nature provides a very straightforward system that can serve as the model for investigating the effects of mineral surface ions on HIN. Our experiments show that the surface (001) of H⁺-exchanged mica displays markedly higher HIN efficiencies than that of Na-/K-mica. Vibrational sum-frequency generation spectroscopy reveals that H-mica induces substantially less orientation ordering than Na-/K-mica within the contact water layer at the interface. Molecular dynamics simulations suggest that the HIN efficiency of mica depends on the positional arrangement and orientation of the interfacial water. The formation of the hexagonal ice Ih basal-type structure in the first water layer atop the mica surface facilitates HIN, which is determined by the size of the protruding ions atop the mica surface and by the surface adsorption energy. The orientational distribution is optimal for HIN when 25% of the water molecules in the first water layer atop the mica surface have one OH group pointing up and 25% have one OH group pointing down, which, in turn, is determined by the surface charge distribution.

Predicting heterogeneous ice nucleation with a data-driven approach

Water in nature predominantly freezes with the help of foreign materials through a process known as heterogeneous ice nucleation. Although this effect was exploited more than seven decades ago in Vonnegut's pioneering cloud seeding experiments, it remains unclear what makes a material a good ice former. Here, we show through a machine learning analysis of nucleation simulations on a database of diverse model substrates that a set of physical descriptors for heterogeneous ice nucleation can be identified. Our results reveal that, beyond Vonnegut's connection with the lattice match to ice, three new microscopic factors help to predict the ice nucleating ability. These are: local ordering induced in liquid water, density reduction of liquid water near the surface and corrugation of the adsorption energy landscape felt by water. With this we take a step towards quantitative understanding of heterogeneous ice nucleation and the in silico design of materials to control ice formation.

Evolution of confined ice nano structures at different levels of pore filling: a synchrotron based X-ray diffraction study

We have thoroughly investigated the crystal structure of ice evolved from super cooled water confined in MCM-41 cylindrical nano pores through a synchrotron-based X-ray diffraction (XRD) technique for two different levels of pore filling. A rigorous analysis of XRD data shows that the nucleation dynamics and the structure of nucleated ice highly depend on the level of pore filling. In the nearly fully hydrated pores, ice crystallites start nucleating inside the pores below 240 K and creep out of the pores to form bulk crystals having crystalline structure of a mixed phase of hexagonal and cubic forms. In the partially hydrated pores, on the other hand, ice crystals cannot creep out of the pore crossing the energy barrier. The crystalline ice particles remaining inside the cylindrical pore show a short range "cubic rich" structure. The "pure cubic" phase has not been identified at either of the pore fillings in these 2.5 nm average size pores. A large fraction of water inside the pores remains in the super cooled liquid phase even at 180 K. This observation is relevant for understanding the ice nucleation through the pore condensation and freezing mechanism, which is a major pathway for the formation of cirrus clouds in the upper atmosphere.

Selective nucleation of ice crystals depending on the inclination angle of nanostructures

Heterogeneous nucleation is decided by many factors, and surface morphology is one of the most important elements. This paper reports the selective ice nucleation and growth process on a series of nanorods with different inclinations, which were rarely mentioned in previous research studies. It is found that the nanorods with special inclinations can cause the selective nucleation of ice crystals because of the spatial geometry matching. On this basis, we can regulate the ice crystal types (mainly including cubic ice and hexagonal ice) accordingly and even improve the freezing efficiency via controlling the inclinations of surface nanorods. In particular, cubic ice occupies the dominant role in the ice crystal on the surface of 45°-inclination nanorods, yet 90°-inclination nanorods are more beneficial for the formation of hexagonal ice. The shape of the nanorods not only controls the type of ice crystal, but also changes the freezing efficiency because different ice crystals have an unequal nucleation energy barrier. There are no apparent differences in the freezing efficiency on nanostructures with 45°, 75° and 90° inclination nanorods, and 60°-inclination nanorods are more favorable for ice nucleation. Our studies can promote the understanding on the selective nucleation of ice crystals and provide a theoretical basis for achieving the regulation of freezing efficiency.

A generalized deep learning approach for local structure identification in molecular simulations

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.

Anomalous Stability of Two-Dimensional Ice Confined in Hydrophobic Nanopores

The freezing of water mostly proceeds via heterogeneous ice nucleation, a process in which an effective nucleation medium not only expedites ice crystallization but also may effectively direct the polymorph selection of ice. Here, we show that water confined within a hydrophobic slit nanopore exhibits a freezing behavior strongly distinguished from its bulk counterpart. Such a difference is reflected by a strong, non-monotonic pore-size dependence of freezing temperature but, more surprisingly, by an unexpected stacking ordering of crystallized two-dimensional ice containing just a few ice layers. In particular, confined trilayer ice is found to exclusively crystallize into a well-ordered, hexagonal stacking sequence despite the fact that nanopore exerts no explicit constraint on stacking order. The absence of cubic stacking sequence is found to be originated from the intrinsically lower thermodynamic stability of cubic ice over hexagonal ice at the interface, which contrasts sharply the nearly degenerated stability of bulk hexagonal and cubic ices. Detailed examination clearly reveals that the divergence is attributed to the inherent difference between the two ice polymorphs in their surface phonon modes, which is further found to generically occur at both hydrophobic and hydrophilic surfaces.

Ice is born in low-mobility regions of supercooled liquid water

From intracellular freezing to cloud formation, the crystallization of water is ubiquitous and shapes life as we know it. A full comprehension of the ice nucleation process at the molecular scale remains elusive and we cannot predict where nucleation will occur. Using computational techniques we show that homogeneous nucleation in supercooled water happens in immobile liquid regions that emerge from heterogeneous dynamics. With this we link the topics of nucleation and dynamical heterogeneity and open ways to understand and control heterogeneous nucleation in solution, in confinement, or at interfaces via understanding their effects on liquid dynamics.

When an ice crystal is born from liquid water, two key changes occur: (i) The molecules order and (ii) the mobility of the molecules drops as they adopt their lattice positions. Most research on ice nucleation (and crystallization in general) has focused on understanding the former with less attention paid to the latter. However, supercooled water exhibits fascinating and complex dynamical behavior, most notably dynamical heterogeneity (DH), a phenomenon where spatially separated domains of relatively mobile and immobile particles coexist. Strikingly, the microscopic connection between the DH of water and the nucleation of ice has yet to be unraveled directly at the molecular level. Here we tackle this issue via computer simulations which reveal that (i) ice nucleation occurs in low-mobility regions of the liquid, (ii) there is a dynamical incubation period in which the mobility of the molecules drops before any ice-like ordering, and (iii) ice-like clusters cause arrested dynamics in surrounding water molecules. With this we establish a clear connection between dynamics and nucleation. We anticipate that our findings will pave the way for the examination of the role of dynamical heterogeneities in heterogeneous and solution-based nucleation.

Unravelling the origins of ice nucleation on organic crystals

Organic molecules such as steroids or amino acids form crystals that can facilitate the formation of ice - arguably the most important phase transition on earth. However, the origin of the ice nucleating ability of organic crystals is still largely unknown. Here, we combine experiments and simulations to unravel the microscopic details of ice formation on cholesterol, a prototypical organic crystal widely used in cryopreservation. We find that cholesterol - which is also a substantial component of cell membranes - is an ice nucleating agent more potent than many inorganic substrates, including the mineral feldspar (one of the most active ice nucleating materials in the atmosphere). Scanning electron microscopy measurements reveal a variety of morphological features on the surfaces of cholesterol crystals: this suggests that the topography of the surface is key to the broad range of ice nucleating activity observed (from -4 to -20 °C). In addition, we show via molecular simulations that cholesterol crystals aid the formation of ice nuclei in a unconventional fashion. Rather than providing a template for a flat ice-like contact layer (as found in the case of many inorganic substrates), the flexibility of the cholesterol surface and its low density of hydrophilic functional groups leads to the formation of molecular cages involving both water molecules and terminal hydroxyl groups of the cholesterol surface. These cages are made of 6- and, surprisingly, 5-membered hydrogen bonded rings of water and hydroxyl groups that favour the nucleation of hexagonal as well as cubic ice (a rare occurrence). We argue that the phenomenal ice nucleating activity of steroids such as cholesterol (and potentially of many other organic crystals) is due to (i) the ability of flexible hydrophilic surfaces to form unconventional ice-templating structures and (ii) the different nucleation sites offered by the diverse topography of the crystalline surfaces. These findings clarify how exactly organic crystals promote the formation of ice, thus paving the way toward deeper understanding of ice formation in soft and biological matter - with obvious reverberations on atmospheric science and cryobiology.

Control and prediction of the organic solid state: a challenge to theory and experiment†

The ability of theoretical chemists to quantitatively model the weak forces between organic molecules is being exploited to predict their crystal structures and estimate their physical properties. Evolving crystal structure prediction methods are increasingly being used to aid the design of organic functional materials and provide information about thermodynamically plausible polymorphs of speciality organic materials to aid, for example, pharmaceutical development. However, the increasingly sophisticated experimental studies for detecting the range of organic solid-state behaviours provide many challenges for improving quantitative theories that form the basis for the computer modelling. It is challenging to calculate the relative thermodynamic stability of different organic crystal structures, let alone understand the kinetic effects that determine which polymorphs can be observed and are practically important. However, collaborations between experiment and theory are reaching the stage of devising experiments to target the first crystallization of new polymorphs or create novel organic molecular materials.

Heterogeneous seeded molecular dynamics as a tool to probe the ice nucleating ability of crystalline surfaces

Ice nucleation plays a significant role in a large number of natural and technological processes, but it is challenging to investigate experimentally because of the small time scales (ns) and short length scales (nm) involved. On the other hand, conventional molecular simulations struggle to cope with the relatively long time scale required for critical ice nuclei to form. One way to tackle this issue is to take advantage of free energy or path sampling techniques. Unfortunately, these are computationally costly. Seeded molecular dynamics is a much less demanding alternative that has been successfully applied already to study the homogeneous freezing of water. However, in the case of heterogeneous ice nucleation, nature's favourite route to form ice, an array of suitable interfaces between the ice seeds and the substrate of interest has to be built, and this is no trivial task. In this paper, we present a Heterogeneous SEEDing (HSEED) approach which harnesses a random structure search framework to tackle the ice-substrate challenge, thus enabling seeded molecular dynamics simulations of heterogeneous ice nucleation on crystalline surfaces. We validate the HSEED framework by investigating the nucleation of ice on (i) model crystalline surfaces, using the coarse-grained mW model, and (ii) cholesterol crystals, employing the fully atomistic TIP4P/ice water model. We show that the HSEED technique yields results in excellent agreement with both metadynamics and forward flux sampling simulations. Because of its computational efficiency, the HSEED method allows one to rapidly assess the ice nucleation ability of whole libraries of crystalline substrates-a long-awaited computational development in, e.g., atmospheric science.