Predicting heterogeneous ice nucleation with a data-driven approach

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.

The molecular scale mechanism of deposition ice nucleation on silver iodide

Heterogeneous ice nucleation is a ubiquitous process in the natural and built environment. Deposition ice nucleation, i.e. heterogeneous ice nucleation that - according to the traditional view - occurs in a subsaturated water vapor environment and in the absence of supercooled water on the solid, ice-forming surface, is among the most important ice formation processes in high-altitude cirrus and mixed-phase clouds. Despite its importance, very little is known about the mechanism of deposition ice nucleation at the microscopic level. This study puts forward an adsorption-based mechanism for deposition ice nucleation through results from a combination of atomistic simulations, experiments and theoretical modelling. One of the most potent laboratory surrogates of ice nucleating particles, silver iodide, is used as a substrate for the simulations. We find that water initially adsorbs in clusters which merge and grow over time to form layers of supercooled water. Ice nucleation on silver iodide requires at minimum the adsorption of 4 molecular layers of water. Guided by the simulations we propose the following fundamental freezing steps: (1) Water molecules adsorb on the surface, forming nanodroplets. (2) The supercooled water nanodroplets merge into a continuous multilayer when they grow to about 3 molecular layers thick. (3) The layer continues to grow until the critical thickness for freezing is reached. (4) The critical ice cluster continues to grow.

Graph-Neural-Network-Based Unsupervised Learning of the Temporal Similarity of Structural Features Observed in Molecular Dynamics Simulations

Classification of molecular structures is a crucial step in molecular dynamics (MD) simulations to detect various structures and phases within systems. Molecular structures, which are commonly identified using order parameters, were recently identified using machine learning (ML), that is, the ML models acquire structural features using labeled crystals or phases via supervised learning. However, these approaches may not identify unlabeled or unknown structures, such as the imperfect crystal structures observed in nonequilibrium systems and interfaces. In this study, we proposed the use of a novel unsupervised learning framework, denoted temporal self-supervised learning (TSSL), to learn structural features and design their parameters. In TSSL, the ML models learn that the structural similarity is learned via contrastive learning based on minor short-term variations caused by perturbations in MD simulations. This learning framework is applied to a sophisticated architecture of graph neural network models that use bond angle and length data of the neighboring atoms. TSSL successfully classifies water and ice crystals based on high local ordering, and furthermore, it detects imperfect structures typical of interfaces such as the water-ice and ice-vapor interfaces.

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'.

Molecular cluster analysis using local order parameters selected by machine learning

Accurately extracting local molecular structures is essential for understanding the mechanisms of phase and structural transitions. A promising method to characterize the local molecular structure is defining the value of the local order parameter (LOP) for each particle. This work develops the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO), a machine learning package that can propose an optimal (set of) LOP(s) quickly and automatically for a huge number of LOP species and various methods of selecting neighboring particles for the calculation. We applied this package to distinguish between the nematic and smectic phases of uniaxial liquid crystal molecules, and selected candidate LOPs that could be used to precisely observe the nematic-smectic phase transition. The LOP candidates were used to observe the nucleation and subsequent percolation transition, and the effect of the choice of LOP species and neighboring particles on the statistics of local molecular structures (clusters) was examined. The procedure revealed the time evolution of the number of clusters and the dependence of the percolation curve on the number of neighboring particles for each LOP species. The LOP species with the lowest dependence on the number of neighboring particles was the best-performing LOP species in the MALIO screening strategy. These results not only show that machine learning can powerfully screen a huge number of LOP species and suggest only a few promising candidates, but also indicate that MALIO can select the best LOP species.

Quantitative Structure-Activity Relationship Studies on Alkane Chemistry Tuning Ice Nucleation

Understanding how surface chemistry influences ice nucleation is essential for both forecasting icing phenomena and designing surfaces with desired ice-control abilities. Although alkylating is one of the most common and simplest ways for surface chemical modification, the effect of alkane chemistry on ice nucleation remains ambiguous as a result of the usually accompanying interferences of substrate morphology or heat transfer. Here, we decouple the effect of alkane chemistry on ice nucleation by investigating the ice nucleation behaviors on alkane self-assembled monolayers (SAMs) with atomic-level roughness and (sub)nanoscale thickness. Our results indicate that the introduction of alkane chemistry leads to decreased ice nucleation activities, i.e., increased anti-icing abilities, and the longer alkyl chain endows the SAM surface with the more inert ability to promote ice nucleation. The alkyl-chain-length-dependent ice nucleation activities are found to be correlated with the surface polarity. This work sheds light on a long-standing question of how alkane chemistry influences ice nucleation and offers a useful strategy for tuning ice nucleation.

Orcinol and resorcinol induce local ordering of water molecules near the liquid-vapor interface

Resorcinol and orcinol are simple members of the family of phenolic compounds present in particulate matter in the atmosphere; they are amphiphilic in nature and thus surface active in aqueous solution. Here, we used X-ray photoelectron spectroscopy to probe the concentration of resorcinol (benzene-1,3-diol) and orcinol (5-methylbenzene-1,3-diol) at the liquid-vapor interface of aqueous solutions. Qualitatively consistent surface propensity and preferential orientation was obtained by molecular dynamics simulations. Auger electron yield near-edge X-ray absorption fine structure (NEXAFS) spectroscopy was used to probe the hydrogen bonding (HB) structure, indicating that the local structure of water molecules near the surface of the resorcinol and orcinol solutions tends towards a larger fraction of tetrahedrally coordinated molecules than observed at the liquid-vapor interface of pure water. The order parameter obtained from the molecular dynamics simulations confirm these observations. This effect is being discussed in terms of the formation of an ordered structure of these molecules at the surface leading to patterns of hydrated OH groups with distances among them that are relatively close to those in ice. These results suggest that the self-assembly of phenolic species at the aqueous solution-air interface could induce freezing similar to the case of fatty alcohol monolayers and, thus, be of relevance for ice nucleation in the atmosphere. We also attempted at looking at the changes of the O 1b₁, 3a₂ and 1b₂ molecular orbitals of liquid water, which are known to be sensitive to the HB structure as well, in response to the presence of resorcinol and orcinol. However, these changes remained negligible within uncertainty for both experimentally obtained valence spectra and theoretically calculated density of states.

The Modification of Polyvinyl Alcohol for Ice Nucleation Based upon the Structures of Antifreeze Glycoproteins Found in Antarctic Fish

Various alternative compounds have been investigated to prevent icing, one of which includes poly(vinyl) alcohol (PVA), which has shown promising anti-freeze effects. However, determining the optimal structures and formulations of PVA for anti-icing applications has remained a challenge. Building upon our previous work, which used molecular dynamics simulations to assess the effects of hydroxyl group separation distance on ice nucleation, in this work, PVA was modified based upon the structures of antifreeze glycoproteins (AFGPs) found in Antarctic fish, and examined as a potential antifreeze compound. Four different PVA samples with different degrees of hydrolysis were fabricated and subsequently examined for their effects on ice crystallization. The results showed that the modified PVA samples with degrees of hydrolysis of 76% and 66% had an effect on ice crystallization, delaying ice crystallization by an average of approximately 20 min, and even preventing ice crystallization altogether in a small portion of the sample. Meanwhile, other samples with degrees of hydrolysis of 100% and 34% either showed no effect on ice crystallization, shortened the ice crystallization time, and appeared to promote ice nucleation.

Silica as a Model Ice-Nucleating Particle to Study the Effects of Crystallinity, Porosity, and Low-Density Surface Functional Groups on Immersion Freezing

Aerosol particles can facilitate heterogeneous ice formation in the troposphere and stratosphere by acting as ice-nucleating particles, modulating cloud formation/dissipation, precipitation, and their microphysical properties. Heterogeneous ice nucleation is driven by ice embryo formation on the particle surface, which can be influenced by features of the surface such as crystallinity, surface structure, lattice structure, defects, and functional groups. To characterize the effect of crystallinity, pores, and surface functional groups toward ice nucleation, samples of comparable silica systems, specifically, quartz, ordered and nonordered porous amorphous silica samples with a range of pore sizes (2-11 nm), and nonporous functionalized silica spheres, were used as models for mineral dust aerosol particles. The ice nucleation activity of these samples was investigated by using an immersion freezing chamber. The results suggest that crystallinity has a larger effect than porosity on ice nucleation activity, as all of the porous silica samples investigated had lower onset freezing temperatures and lower ice nucleation activities than quartz. Our findings also suggest that pores alone are not sufficient to serve as effective active sites and need some additional chemical or physical property, like crystallinity, to nucleate ice in immersion mode freezing. The addition of a low density of organic functional groups to nonporous samples showed little enhancement compared to the inherent nucleation activity of silica with native surface hydroxyl groups. The density of functional groups investigated in this work suggests that a different arrangement of surface groups may be needed for enhanced immersion mode ice nucleation activity. In summary, crystallinity dictates the ice nucleation activity of silica samples rather than porosity or low-density surface functional groups. This work has broader implications regarding the climate impacts resulting from ice cloud formation.

Accurate prediction of ice nucleation from room temperature water

Crystal nucleation is one of the most fundamental processes in the physical sciences and almost always occurs heterogeneously with the aid of a nucleating substrate. No example of nucleation is more ubiquitous and impactful than the formation of ice, vital to fields as diverse as geology, biology, aeronautics, and climate science. However, despite considerable effort, we still cannot predict a priori the efficacy of a nucleating agent. Here we utilize deep learning methods to accurately predict nucleation ability from images of room temperature liquid water-generated from molecular dynamics simulations-on a broad range of substrates. The resulting model, named IcePic, can rapidly and accurately infer nucleation ability, eliminating the requirement for either notoriously expensive simulations or direct experimental measurement. In an online poll, IcePic was found to significantly outperform humans in predicting the ice nucleating efficacy of materials. By analyzing the typical errors made by humans, as well as the application of reverse interpretation methods, physical insights into the role the water contact layer plays in ice nucleation have been obtained. Moving forward, we suggest that IcePic can be used as an easy, cheap, and rapid way to discern the nucleation ability of substrates, also with potential for learning other properties related to interfacial water.

Chemical Nature of Heterogeneous Electrofreezing of Supercooled Water Revealed on Polar (Pyroelectric) Surfaces

The ability to control the icing temperature of supercooled water (SCW) is of supreme importance in subfields of pure and applied sciences. The ice freezing of SCW can be influenced heterogeneously by electric effects, a process known as electrofreezing. This effect was first discovered during the 19th century; however, its mechanism is still under debate. In this Account we demonstrate, by capitalizing on the properties of polar crystals, that heterogeneous electrofreezing of SCW is a chemical process influenced by an electric field and specific ions. Polar crystals possess a net dipole moment. In addition, they are pyroelectric, displaying short-lived surface charges at their hemihedral faces at the two poles of the crystals as a result of temperature changes. Accordingly, during cooling or heating, an electric field is created, which is negated by the attraction of compensating charges from the environment. This process had an impact in the following experiments. The icing temperatures of SCW within crevices of polar crystals are higher in comparison to icing temperatures within crevices of nonpolar analogs. The role played by the electric effect was extricated from other effects by the performance of icing experiments on the surfaces of pyroelectric quasi-amorphous SrTiO₃. During those studies it was found that on positively charged surfaces the icing temperature of SCW is elevated, whereas on negatively charged surfaces it is reduced. Following investigations discovered that the icing temperature of SCW is impacted by an ionic current created within a hydrated layer on top of hydrophilic faces residing parallel to the polar axes of the crystals. In the absence of such current on analogous hydrophobic surfaces, the pyroelectric effect does not influence the icing temperature of SCW. Those results implied that electrofreezing of SCW is a process influenced by specific compensating ions attracted by the pyroelectric field from the aqueous solution. When freezing experiments are performed in an open atmosphere, bicarbonate and hydronium ions, created by the dissolution of atmospheric CO₂ in water, influence the icing temperature. The bicarbonate ions, when attracted by positively charged pyroelectric surfaces, elevate the icing temperature, whereas their counterparts, hydronium ions, when attracted by the negatively charged surfaces reduce the icing temperature. Molecular dynamic simulations suggested that bicarbonate ions, concentrated within the near positively charged interfacial layer, self-assemble with water molecules to create stabilized slightly distorted “ice-like” hexagonal assemblies which mimic the hexagons of the crystals of ice. This occurs by replacing, within those ice-like hexagons, two hydrogen bonds of water by C–O bonds of the HCO₃^– ion. On the basis of these simulations, it was predicted and experimentally confirmed that other trigonal planar ions such as NO₃^–, guanidinium⁺, and the quasi-hexagonal biguanidinium⁺ ion elevate the icing temperature. These ions were coined as “ice makers”. Other ions including hydronium, Cl^–, and SO₄^–2 interfere with the formation of ice-like assemblies and operate as “ice breakers”. The higher icing temperatures induced within the crevices of the hydrophobic polar crystals in comparison to the nonpolar analogs can be attributed to the proton ordering of the water molecules. In contrast, the icing temperatures on related hydrophilic surfaces are influenced both by compensating charges and by proton ordering.

Template-Induced Precursor Formation in Heterogeneous Nucleation: Controlling Polymorph Selection and Nucleation Efficiency

We present an atomistic study of heterogeneous nucleation in Ni employing transition path sampling, which reveals a template precursor-mediated mechanism of crystallization. Most notably, we find that the ability of tiny templates to modify the structural features of the liquid and promote the formation of precursor regions with enhanced bond-orientational order is key to determining their nucleation efficiency and the polymorphs that crystallize. Our results reveal an intrinsic link between structural liquid heterogeneity and the nucleating ability of templates, which significantly advances our understanding toward the control of nucleation efficiency and polymorph selection.

Antifreeze proteins and their biomimetics for cell cryopreservation: Mechanism, function and application-A review

Cell-based therapy is a promising technology for intractable diseases and health care applications, in which cryopreservation has become an essential procedure to realize the production of therapeutic cells. Ice recrystallization is the major factor that affects the post-thaw viability of cells. As a typical series of biomacromolecules with ice recrystallization inhibition (IRI) activity, antifreeze proteins (AFPs) have been employed in cell cryopreservation. Meanwhile, synthesized materials with IRI activity have emerged in the name of biomimetics of AFPs to expand their availability and practicality. However, fabrication of AFPs mimetics is in a chaotic period. There remains little commonality among different AFPs mimetics, then it is difficult to set guidelines on their design. With no doubt, a comprehensive understanding on the antifreezing mechanism of AFPs in molecular level will enable us to rebuild the function of AFPs, and provide convenience to clarify the relationship between structure and function of these early stage biomimetics. In this review, we would discuss those previously reported biomimetics to summarize their structure characteristics concerning the IRI activity and attempt to develop a roadmap for guiding the design of novel AFPs mimetics.

Fcc vs. hcp competition in colloidal hard-sphere nucleation: on their relative stability, interfacial free energy and nucleation rate

Hard-sphere crystallization has been widely investigated over the last six decades by means of colloidal suspensions and numerical methods. However, some aspects of its nucleation behaviour are still under debate. Here, we provide a detailed computational characterisation of the polymorphic nucleation competition between the face-centered cubic (fcc) and the hexagonal-close packed (hcp) hard-sphere crystal phases. By means of several state-of-the-art simulation techniques, we evaluate the melting pressure, chemical potential difference, interfacial free energy and nucleation rate of these two polymorphs, as well as of a random stacking mixture of both crystals. Our results highlight that, despite the fact that both polymorphs have very similar stability, the interfacial free energy of the hcp phase could be marginally higher than that of the fcc solid, which in consequence, mildly decreases its propensity to nucleate from the liquid compared to the fcc phase. Moreover, we analyse the abundance of each polymorph in grown crystals from different types of inserted nuclei: fcc, hcp and stacking disordered fcc/hcp seeds, as well as from those spontaneously emerged from brute force simulations. We find that post-critical crystals fundamentally grow maintaining the polymorphic structure of the critical nucleus, at least until moderately large sizes, since the only crystallographic orientation that allows stacking close-packed disorder is the fcc (111) plane, or equivalently the hcp (0001) one. Taken together, our results contribute with one more piece to the intricate puzzle of colloidal hard-sphere crystallization.

Computational Assessment of Modified Antifreeze Glycoproteins on Ice Nucleation

Antifreeze glycoproteins (AFGPs) found in various fish are used by the organisms to prevent freezing. While these compounds have been studied for their ability to bind to, and prevent the complete crystallization of water, the exact mechanisms by which AFGPs prevent freezing are still undetermined. Therefore, building upon our previous work, this study uses molecular dynamics simulations to assess the effects of hydroxyl group separation distance on AFGP ice nucleation activity. Water droplet crystallization simulations showed that modified AFGP structures containing hydroxyl distances smaller than ~3.0 Å lost their ability to prevent ice crystallization. Furthermore, modified AFGP containing hydroxyl distances of 7.327 Å and 6.160 Å was correlated with a promotion in ice nucleation, as demonstrated by the changes in the energy of the system. This supports the notion that the distance, and therefore, geometry characteristics between the hydroxyl groups located on the saccharide structures play a key role in the ice crystallization inhibition properties of AFGP compounds.

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.

Exploring CO2 @sI Clathrate Hydrates as CO2 Storage Agents by Computational Density Functional Approaches

The formation of specific clathrate hydrates and their transformation at given thermodynamic conditions depends on the interactions between the guest molecule/s and the host water lattice. Understanding their structural stability is essential to control structure-property relations involved in different technological applications. Thus, the energetic aspects relative to CO₂ @sI clathrate hydrate are investigated through the computation of the underlying interactions, dominated by hydrogen bonds and van der Waals forces, from first-principles electronic structure approaches. The stability of the CO₂ @sI clathrate is evaluated by combining bottom-up and top-down approaches. Guest-free and CO₂ guest-filled aperiodic cages, up to the gradually CO₂ occupation of the entire sI periodic unit cells were considered. Saturation, cohesive and binding energies for the systems are determined by employing a variety of density functionals and their performance is assessed. The dispersion corrections on the non-covalent interactions are found to be important in the stabilization of the CO₂ @sI energies, with the encapsulation of the CO₂ into guest-free/empty cage/lattice being always an energetically favorable process for most of the functionals studied. The PW86PBE functional with XDM or D3(BJ) dispersion corrections predicts a lattice constant in accord to the experimental values available, and simultaneously provides a reliable description for the guest-host interactions in the periodic CO₂ @sI crystal, as well as the energetics of its progressive single cage occupancy process. It has been found that the preferential orientation of the single CO₂ in the large sI crystal cages has a stabilizing effect on the hydrate, concluding that the CO₂ @sI structure is favored either by considering the individual building block cages or the complete sI unit cell crystal. Such benchmark and methodology cross-check studies benefit new data-driven model research by providing high-quality training information, with new insights that indicate the underlying factors governing their structure-driven stability, and triggering further investigations for controlling the stabilization of these promising long-term CO₂ storage materials.

Homogeneous nucleation of sheared liquids: advances and insights from simulations and theory

One of the most ubiquitous and technologically important phenomena in nature is the nucleation of homogeneous flowing systems. The microscopic effects of shear on a nucleating system are still imperfectly understood, although in recent years a consistent picture has emerged. The opposing effects of shear can be split into two major contributions for simple atomic and molecular liquids: increase of the energetic cost of nucleation, and enhancement of the kinetics. In this perspective, we describe the latest computational and theoretical techniques which have been developed over the past two decades. We collate and unify the overarching influences of shear, temperature, and supersaturation on the process of homogeneous nucleation. Experimental techniques and capabilities are discussed, against the backdrop of results from simulations and theory. Although we primarily focus on simple systems, we also touch upon the sheared nucleation of more complex systems, including glasses and polymer melts. We speculate on the promising directions and possible advances that could come to fruition in the future.