Deciphering the controlling factors for phase transitions in zeolitic imidazolate frameworks

Zeolitic imidazolate frameworks (ZIFs) feature complex phase transitions, including polymorphism, melting, vitrification, and polyamorphism. Experimentally probing their structural evolution during transitions involving amorphous phases is a significant challenge, especially at the medium-range length scale. To overcome this challenge, here we first train a deep learning-based force field to identify the structural characteristics of both crystalline and non-crystalline ZIF phases. This allows us to reproduce the structural evolution trend during the melting of crystals and formation of ZIF glasses at various length scales with an accuracy comparable to that of ab initio molecular dynamics, yet at a much lower computational cost. Based on this approach, we propose a new structural descriptor, namely, the ring orientation index, to capture the propensity for crystallization of ZIF-4 (Zn(Im)2, Im = C3H3N2-) glasses, as well as for the formation of ZIF-zni (Zn(Im)2) out of the high-density amorphous phase. This crystal formation process is a result of the reorientation of imidazole rings by sacrificing the order of the structure around the zinc-centered tetrahedra. The outcomes of this work are useful for studying phase transitions in other metal-organic frameworks (MOFs) and may thus guide the development of MOF glasses.

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space. Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials. This work pioneers a bottom-down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

Predicting rare earth elements concentration in coal ashes with multi-task neural networks

The increasing demand for rare earth elements (REEs) makes them a scarce strategic resource for technical developments. In that regard, harvesting REEs from coal ashes-a waste byproduct from coal power plants-offers an alternative solution to conventional ore-based extraction. However, this approach is bottlenecked by our ability to screen coal ashes bearing large concentrations of REEs from feedstocks-since measuring the REE content in ashes is a time-consuming and costly task requiring advanced analytical tools. Here, we propose a machine learning approach to predict the REE contents based on the bulk composition of coal ashes, easily measurable under the routine testing protocol. We introduce a multi-task neural network that simultaneously predicts the contents of different REEs. Compared to the single-task model, this model exhibits notably improved accuracy and reduced sensitivity to noise. Further model analyses reveal key data patterns for screening coal ashes with high REE concentrations. Additionally, we showcase the utilization of transfer learning to improve the adaptability of our model to coal ashes from a distinct source.

Atomistic origin of kinetics in hydrated aluminosilicate gels upon precipitation

Calcium-alumino-silicate-hydrate (CaO-Al2O3-SiO2-H2O, or C-A-S-H) gel, which is the binding phase of cement-based materials, greatly influences concrete mechanical properties and durability. However, the atomic-scale kinetics of the aluminosilicate network condensation remains puzzling. Here, based on reactive molecular dynamics simulations of C-A-S-H systems formation with varying Al/Ca molar ratios, we study the kinetic mechanism of the hydrated aluminosilicate gels upon precipitation. We show that the condensation activation energy decreases with the Al/Ca molar ratio, which suggests that the concentration of the Al polytopes has a great effect on controlling the kinetics of the gelation reaction. Significantly, we demonstrate that 5-fold Al atoms are mainly forming at high Al/Ca molar ratios since there are insufficient hydrogen cations or extra calcium cations to compensate the negatively charged Al polytopes at high Al/Ca molar ratios during accelerated aging.

Fly ash degree of reaction in hypersaline NaCl and CaCl2 brines: Effects of calcium-based additives

The pozzolanic reaction of fly ashes with calcium-based additives can be effectively used to solidify and chemically stabilize (S&S process) highly concentrated brines inside a cementitious matrix. However, complex interactions between the fly ash, the additive, and the brine typically affect the phases formed at equilibrium, and the resulting solid capacity to successfully encapsulate the brine and its contaminants. Here, the performances of two types of fly ash (a Class C and Class F fly ash) are assessed when combined with different additives (two types of cement, or lime with and without NaAlO2), and two types of brine (NaCl or CaCl2) over a range of concentrations (0 ≤ [Cl-] ≤ 2 M). The best performing matrices - i.e., the matrices with the highest Cl-containing phases content - were identified using XRD and TGA. The experimental results were then combined with thermodynamic modeling to dissociate the contribution of the fly ash from that of the additives. All results were implemented in a machine learning model that showed good accuracy at predicting the fly ash degree of reaction, allowing for the robust prediction of extended systems performance when combined with thermodynamic modeling.

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator

Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to "bypass all physics laws" to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.

Electrosteric Control of the Aggregation and Yielding Behavior of Concentrated Portlandite Suspensions

Portlandite (calcium hydroxide: CH: Ca(OH)2) suspensions aggregate spontaneously and form percolated fractal aggregate networks when dispersed in water. Consequently, the viscosity and yield stress of portlandite suspensions diverge at low particle loadings, adversely affecting their processability. Even though polycarboxylate ether (PCE)-based comb polyelectrolytes are routinely used to alter the particle dispersion state, water demand, and rheology of similar suspensions (e.g., ordinary portland cement suspensions) that feature a high pH and high ionic strength, their use to control portlandite suspension rheology has not been elucidated. This study combines adsorption isotherms and rheological measurements to elucidate the role of PCE composition (i.e., charge density, side chain length, and grafting density) in controlling the extent of PCE adsorption, particle flocculation, suspension yield stress, and thermal response of portlandite suspensions. We show that longer side-chain PCEs are more effective in affecting suspension viscosity and yield stress, in spite of their lower adsorption saturation limit and fractional adsorption. The superior steric hindrance induced by the longer side chain PCEs results in better efficacy in mitigating particle aggregation even at low dosages. However, when dosed at optimal dosages (i.e., a dosage that induces a dynamically equilibrated dispersion state of particle aggregates), different PCE-dosed portlandite suspensions exhibit identical fractal structuring and rheological behavior regardless of the side chain length. Furthermore, it is shown that the unusual evolution of the rheological response of portlandite suspensions with temperature can be tailored by adjusting the PCE dosage. The ability of PCEs to modulate the rheology of aggregating charged particle suspensions can be generally extended to any colloidal suspension with a strong screening of repulsive electrostatic interactions.

The Crystallization of Disordered Materials under Shock Is Governed by Their Network Topology

When the shock load is applied, materials experience incredibly high temperature and pressure conditions on picosecond timescales, usually accompanied by remarkable physical or chemical phenomena. Understanding the underlying physics that governs the kinetics of shocked materials is of great importance for both physics and materials science. Here, combining experiment and large-scale molecular dynamics simulation, the ultrafast nanoscale crystal nucleation process in shocked soda-lime silicate glass is investigated. By adopting topological constraints theory, this study finds that the propensity of nucleation is governed by the connectivity of the atomic network. The densification of local networks, which appears once the crystal starts to grow, results in the underconstrained shell around the crystal and prevents further crystallization. These results shed light on the nanoscale crystallization mechanism of shocked materials from the viewpoint of topological constraint theory.

Water as a Modifier in a Hybrid Coordination Network Glass

Chemical diversification of hybrid organic-inorganic glasses remains limited, especially compared to traditional oxide glasses, for which property tuning is possible through addition of weakly bonded modifier cations. In this work, it is shown that water can depolymerize polyhedra with labile metal-ligand bonds in a cobalt-based coordination network, yielding a series of nonstoichiometric glasses. Calorimetric, spectroscopic, and simulation studies demonstrate that the added water molecules promote the breakage of network bonds and coordination number changes, leading to lower melting and glass transition temperatures. These structural changes modify the physical and chemical properties of the melt-quenched glass, with strong parallels to the "modifier" concept in oxides. It is shown that this approach also applies to other transition metal-based coordination networks, and it will thus enable diversification of hybrid glass chemistry, including nonstoichiometric glass compositions, tuning of properties, and a significant rise in the number of glass-forming hybrid systems by allowing them to melt before thermal decomposition.

Metal cations as inorganic structure-directing agents during the synthesis of phillipsite and tobermorite

Metal cation identity determines the zeolite topology. Framework topology determines the total zeolite cationic content. Potassium predominantly counterbalances Al anions; sodium and calcium are predominantly structure-directing agents.

New insights into the early stage nucleation of calcium carbonate gels by reactive molecular dynamics simulations

The precipitation of calcium carbonate (CaCO₃) is a key mechanism in carbon capture applications relying on mineralization. In that regard, Ca-rich cementitious binders offer a unique opportunity to act as a large-scale carbon sink by immobilizing CO₂ as calcium carbonate by mineralization. However, the atomistic mechanism of calcium carbonate formation is still not fully understood. Here, we study the atomic scale nucleation mechanism of an early stage amorphous CaCO₃ gel based on reactive molecular dynamics (MD) simulations. We observe that reactive MD offers a notably improved description of this reaction as compared to classical MD, which allows us to reveal new insights into the structure of amorphous calcium carbonate gels and formation kinetics thereof.

Resolving the Conflict between Strength and Toughness in Bioactive Silica-Polymer Hybrid Materials

Simultaneously improving the strength and toughness of materials is a major challenge. Inorganic-polymer hybrids offer the potential to combine mechanical properties of a stiff inorganic glass with a flexible organic polymer. However, the toughening mechanism at the atomic scale remains largely unknown. Based on combined experimental and molecular dynamics simulation results, we find that the deformation and fracture behavior of hybrids are governed by noncovalent intermolecular interactions between polymer and silica networks rather than the breakage of covalent bonds. We then attempt three methods to improve the balance between strength and toughness of hybrids, namely the total inorganic/organic (I/O) weight ratio, the size of silica nanoparticles, and the ratio of -C-O vs -C-C bonds in the polymer chains. Specifically, for a hybrid with matched silica size and I/O ratio, we demonstrate optimized mechanical properties in terms of strength (1.75 MPa at breakage), degree of elongation at the fracture point (31%), toughness (219 kPa), hardness (1.08 MPa), as well as Young's modulus (3.0 MPa). We also demonstrate that this hybrid material shows excellent biocompatibility and ability to support cell attachment as well as proliferation. This supports the possible application of this material as a strong yet tough bone scaffold material.

Beyond the Average: Spatial and Temporal Fluctuations in Oxide Glass-Forming Systems

Atomic structure dictates the performance of all materials systems; the characteristic of disordered materials is the significance of spatial and temporal fluctuations on composition-structure-property-performance relationships. Glass has a disordered atomic arrangement, which induces localized distributions in physical properties that are conventionally defined by average values. Quantifying these statistical distributions (including variances, fluctuations, and heterogeneities) is necessary to describe the complexity of glass-forming systems. Only recently have rigorous theories been developed to predict heterogeneities to manipulate and optimize glass properties. This article provides a comprehensive review of experimental, computational, and theoretical approaches to characterize and demonstrate the effects of short-, medium-, and long-range statistical fluctuations on physical properties (e.g., thermodynamic, kinetic, mechanical, and optical) and processes (e.g., relaxation, crystallization, and phase separation), focusing primarily on commercially relevant oxide glasses. Rigorous investigations of fluctuations enable researchers to improve the fundamental understanding of the chemistry and physics governing glass-forming systems and optimize structure-property-performance relationships for next-generation technological applications of glass, including damage-resistant electronic displays, safer pharmaceutical vials to store and transport vaccines, and lower-attenuation fiber optics. We invite the reader to join us in exploring what can be discovered by going beyond the average.

Experimental evidence of auxeticity in ion implanted single crystal calcite

We report initial experimental evidence of auxeticity in calcite by ion implanting (1010) oriented single crystalline calcite with Ar⁺ at room temperature using an ion energy of 400 keV and a dose of 1 × 10¹⁴ cm⁻². Lattice compression normal to the substrate surface was observed, which is an atypical result for ion implanted materials. The auxetic behavior is consistent with predictions that indicate auxeticity had been predicted along two crystallographic directions including [1010]. Materials with a positive Poisson’s ratio experience lattice expansion normal to the substrate surface when ion implanted, whereas lattice contraction normal to the surface is evidence of auxetic behavior. Triple-axis X-ray diffraction measurements confirmed the auxetic strain state of the implanted calcite substrates. Reciprocal space maps for the symmetric 3030 and asymmetric 1450 reflections revealed that the implanted region was fully strained (pseudomorphic) to the bulk of the substrate, as is typical with implanted single crystals. A symmetric (3030) ω:2θ line scan was used with X-ray dynamical diffraction simulations to model the strain profile and extract the variation of compressive strain as a function of depth normal to the substrate surface. SRIM calculations were performed to obtain a displacement-per-atom profile and implanted Ar⁺ concentration profile. It was found that the strain profile matches the displacement-per-atom profile. This study demonstrated the use of ion implantation and X-ray diffraction methods to probe mechanical properties of materials and to test predictions such as the auxeticity.

Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

Thin films of amorphous alumina (a-Al₂O₃) have recently been found to deform permanently up to 100% elongation without fracture at room temperature. If the underlying ductile deformation mechanism can be understood at the nanoscale and exploited in bulk samples, it could help to facilitate the design of damage-tolerant glassy materials, the holy grail within glass science. Here, based on atomistic simulations and classification-based machine learning, we reveal that the propensity of a-Al₂O₃ to exhibit nanoscale ductility is encoded in its static (nonstrained) structure. By considering the fracture response of a series of a-Al₂O₃ systems quenched under varying pressure, we demonstrate that the degree of nanoductility is correlated with the number of bond switching events, specifically the fraction of 5- and 6-fold coordinated Al atoms, which are able to decrease their coordination numbers under stress. In turn, we find that the tendency for bond switching can be predicted based on a nonintuitive structural descriptor calculated based on the static structure, namely, the recently developed "softness" metric as determined from machine learning. Importantly, the softness metric is here trained from the spontaneous dynamics of the system (i.e., under zero strain) but, interestingly, is able to readily predict the fracture behavior of the glass (i.e., under strain). That is, lower softness facilitates Al bond switching and the local accumulation of high-softness regions leads to rapid crack propagation. These results are helpful for designing glass formulations with improved resistance to fracture.

Experimental method to quantify the ring size distribution in silicate glasses and simulation validation thereof

Silicate glasses have no long-range order and exhibit a short-range order that is often fairly similar to that of their crystalline counterparts. Hence, the out-of-equilibrium nature of glasses is largely encoded in their medium-range order. However, the ring size distribution-the key feature of silicate glasses' medium-range structure-remains invisible to conventional experiments and, hence, is largely unknown. Here, by combining neutron diffraction experiments and force-enhanced atomic refinement simulations for two archetypical silicate glasses, we show that rings of different sizes exhibit a distinct contribution to the first sharp diffraction peak in the structure factor. On the basis of these results, we demonstrate that the ring size distribution of silicate glasses can be determined solely from neutron diffraction patterns, by analyzing the shape of the first sharp diffraction peak. This method makes it possible to uncover the nature of silicate glasses' medium-range order.

Topological origin of phase separation in hydrated gels

HYPOTHESIS

Depending on their composition, hydrated gels can be homogeneous or phase-separated, which, in turn, affects their dynamical and mechanical properties. However, the nature of the structural features, if any, that govern the propensity for a given gel to phase-separate remains largely unknown. Here, we argue that the propensity for hydrated gels to phase-separate is topological in nature.

SIMULATIONS

We employ reactive molecular dynamics simulations to model the early-age precipitation of calcium-alumino-silicate-hydrate (CASH) gels with varying compositions, i.e., (CaO)_1.7(Al₂O₃)_x(SiO₂)_{1 -x}(H₂O)_{3.7 +x}. By adopting topological constraint theory, we investigate the structural origin of phase separation in hydrated gels.

FINDINGS

We report the existence of a homogeneous-to-phase-separated transition, wherein Si-rich (x ≤ 0.10) CASH gels are homogeneous, whereas Al-rich (x > 0.10) CASH gels tend to phase-separate. Furthermore, we demonstrate that this transition is correlated to a topological flexible-to-rigid transition within the atomic network. We reveal that the propensity for topologically-overconstrained gels to phase-separate arises from the existence of some internal stress within their atomic network, which acts as an energy penalty that drives phase separation.

Bond Switching in Densified Oxide Glass Enables Record-High Fracture Toughness

Humans primarily interact with information technology through glass touch screens, and the world would indeed be unrecognizable without glass. However, the low toughness of oxide glasses continues to be their Achilles heel, limiting both future applications and the possibility to make thinner, more environmentally friendly glasses. Here, we show that with proper control of plasticity mechanisms, record-high values of fracture toughness for transparent bulk oxide glasses can be achieved. Through proper combination of gas-mediated permanent densification and rational composition design, we increase the glasses' propensity for plastic deformation. Specifically, we demonstrate a fracture toughness of an aluminoborate glass (1.4 MPa m^0.5) that is twice as high as that of commercial glasses for mobile devices. Atomistic simulations reveal that the densification of the adaptive aluminoborate network increases coordination number changes and bond swapping, ultimately enhancing plasticity and toughness upon fracture. Our findings thus provide general insights into the intrinsic toughening mechanisms of oxide glasses.

Interatomic potential parameterization using particle swarm optimization: Case study of glassy silica

Classical molecular dynamics simulations of glassy materials rely on the availability of accurate yet computationally efficient interatomic force fields. The parameterization of new potentials remains challenging due to the non-convex nature of the accompanying optimization problem, which renders the traditional optimization methods inefficient or subject to bias. In this study, we present a new parameterization method based on particle swarm optimization (PSO), which is a stochastic population-based optimization method. Using glassy silica as a case study, we introduce two interatomic potentials using PSO, which are parameterized so as to match structural features obtained from ab initio simulations and experimental neutron diffraction data. We find that the PSO algorithm is highly efficient at searching for and identifying viable potential parameters that reproduce the structural features used as the target in the parameterization. The presented approach is very general and can be easily applied to other interatomic potential parameterization schemes.

The energy landscape governs ductility in disordered materials

Based on their structure, non-crystalline phases can fail in a brittle or ductile fashion. However, the nature of the link between structure and propensity for ductility in disordered materials has remained elusive. Here, based on molecular dynamics simulations of colloidal gels and silica glasses, we investigate how the degree of structural disorder affects the fracture of disordered materials. As expected, we observe that structural disorder results in an increase in ductility. By applying the activation-relaxation technique (an open-ended saddle point search algorithm), we demonstrate that the propensity for ductility is controlled by the topography of the energy landscape. Interestingly, we observe a power-law relationship between the particle non-affine displacement upon fracture and the average local energy barrier. This reveals that the dynamics of the particles upon fracture is encoded in the static energy landscape, i.e., before any load is applied. This relationship is shown to apply to several classes of non-crystalline materials (oxide and metallic glasses, amorphous solid, and colloidal gels), which suggests that it may be a generic feature of disordered materials.

Bulk Metallic Glasses' Response to Oscillatory Stress Is Governed by the Topography of the Energy Landscape

When subjected to cyclic loading, bulk metallic glasses tend to exhibit fatigue-induced damage. Although fatigue is a key limitation of metallic glasses, its atomic origin remains elusive. Here, based on molecular dynamics simulations, we investigate the response of metallic glasses prepared with varying cooling rates to oscillatory stress. We find that fatigue manifests itself as an accumulation of residual strain, which results from some nonaffine displacement of the atoms. Such local reorganizations are promoted under a high cooling rate. Importantly, we demonstrate that the fatigue-induced dynamics of the atoms is encoded in the topography of the static energy landscape, i.e., before any load is applied.

Atomic Dislocations and Bond Rupture Govern Dissolution Enhancement under Acoustic Stimulation

By focusing the power of sound, acoustic stimulation (i.e., often referred to as sonication) enables numerous "green chemistry" pathways to enhance chemical reaction rates, for instance, of mineral dissolution in aqueous environments. However, a clear understanding of the atomistic mechanism(s) by which acoustic stimulation promotes mineral dissolution remains unclear. Herein, by combining nanoscale observations of dissolving surface topographies using vertical scanning interferometry, quantifications of mineral dissolution rates via analysis of solution compositions using inductively coupled plasma optical emission spectrometry, and classical molecular dynamics simulations, we reveal how acoustic stimulation induces dissolution enhancement. Across a wide range of minerals (Mohs hardness ranging from 3 to 7, surface energy ranging from 0.3 to 7.3 J/m², and stacking fault energy ranging from 0.8 to 10.0 J/m²), we show that acoustic fields enhance mineral dissolution rates (reactivity) by inducing atomic dislocations and/or atomic bond rupture. The relative contributions of these mechanisms depend on the mineral's underlying mechanical properties. Based on this new understanding, we create a unifying model that comprehensively describes how cavitation and acoustic stimulation processes affect mineral dissolution rates.

Temperature-Induced Aggregation in Portlandite Suspensions

Temperature is well known to affect the aggregation behavior of colloidal suspensions. This paper elucidates the temperature dependence of the rheology of portlandite (calcium hydroxide: Ca(OH)₂) suspensions that feature a high ionic strength and a pH close to the particle's isoelectric point. In contrast to the viscosity of the suspending medium (saturated solution of Ca(OH)₂ in water), the viscosity of Ca(OH)₂ suspensions is found to increase with elevating temperature. This behavior is shown to arise from the temperature-induced aggregation of polydisperse Ca(OH)₂ particulates because of the diminution of electrostatic repulsive forces with increasing temperature. The temperature dependence of the suspension viscosity is further shown to diminish with increasing particle volume fraction as a result of volumetric crowding and the formation of denser fractal structures in the suspension. Significantly, the temperature-dependent rheological response of suspensions is shown to be strongly affected by the suspending medium's properties, including ionic strength and ion valence, which affect aggregation kinetics. These outcomes provide new insights into aggregation processes that affect the temperature-dependent rheology of portlandite-based and similar suspensions that feature strong charge screening behavior.

Revealing hidden medium-range order in amorphous materials using topological data analysis

Despite the numerous technological applications of amorphous materials, such as glasses, the understanding of their medium-range order (MRO) structure-and particularly the origin of the first sharp diffraction peak (FSDP) in the structure factor-remains elusive. Here, we use persistent homology, an emergent type of topological data analysis, to understand MRO structure in sodium silicate glasses. To enable this analysis, we introduce a self-consistent categorization of rings with rigorous geometrical definitions of the structural entities. Furthermore, we enable quantitative comparison of the persistence diagrams by computing the cumulative sum of all points weighted by their lifetime. On the basis of these analysis methods, we show that the approach can be used to deconvolute the contributions of various MRO features to the FSDP. More generally, the developed methodology can be applied to analyze and categorize molecular dynamics data and understand MRO structure in any class of amorphous solids.

Precipitation of calcium-alumino-silicate-hydrate gels: The role of the internal stress

Concrete gains its strength from the precipitation of a calcium-alumino-silicate-hydrate (C-A-S-H) colloidal gel, which acts as its binding phase. However, despite concrete's ubiquity in the building environment, the atomic-scale mechanism of C-A-S-H precipitation is still unclear. Here, we use reactive molecular dynamics simulations to model the early-age precipitation of a C-A-S-H gel. We find that, upon gelation, silicate and aluminate precursors condensate and polymerize to form an aluminosilicate gel network. Notably, we demonstrate that the gelation reaction is driven by the existence of a mismatch of atomic-level internal stress between Si and Al polytopes, which are initially experiencing some local tension and compression, respectively. The polymerization of Si and Al polytopes enables the release of these competitive stresses.

Dynamic and stress signatures of the rigid intermediate phase in glass-forming liquids

We study the evolution of enthalpic changes across the glass transition of model sodium silicate glasses (Na₂O)_x(SiO₂)_100-x, focusing on the detection of a flexible-rigid transition and a possible reversibility window in relationship with dynamic properties. We show that the hysteresis resulting from enthalpic relaxation during a numerical cooling-heating cycle is minimized for 12% ≤ x ≤ 20% Na₂O, which echoes with the experimental observation. The key result is the identification of the physical features driving this anomalous behavior. The intermediate-flexible boundary is associated with a dynamic onset with increasing depolymerization that enhances the growing atomic motion with a reduced internal stress, whereas the intermediate-stressed rigid boundary exhibits a substantial increase in the temperature at which the relaxation is maximum. These results suggest an essentially dynamic origin for the intermediate phase observed in network glass-forming liquids.

Fracture toughness of a metal-organic framework glass

Metal-organic framework glasses feature unique thermal, structural, and chemical properties compared to traditional metallic, organic, and oxide glasses. So far, there is a lack of knowledge of their mechanical properties, especially toughness and strength, owing to the challenge in preparing large bulk glass samples for mechanical testing. However, a recently developed melting method enables fabrication of large bulk glass samples (>25 mm³) from zeolitic imidazolate frameworks. Here, fracture toughness (K_Ic) of a representative glass, namely ZIF-62 glass (Zn(C₃H₃N₂)_1.75(C₇H₅N₂)_0.25), is measured using single-edge precracked beam method and simulated using reactive molecular dynamics. K_Ic is determined to be ~0.1 MPa m^0.5, which is even lower than that of brittle oxide glasses due to the preferential breakage of the weak coordinative bonds (Zn-N). The glass is found to exhibit an anomalous brittle-to-ductile transition behavior, considering its low fracture surface energy despite similar Poisson’s ratio to that of many ductile metallic and organic glasses.

Metal-organic framework glasses are gaining interest, but large samples are difficult to fabricate and mechanical properties are not well understood. Here, the authors use experiments and simulations to assess fracture toughness and flexural strength of a zeolitic imidazolate framework glass.

How clay particulates affect flow cessation and the coiling stability of yield stress-matched cementing suspensions

The remarkable increase in the flow resistance of dense suspensions can hinder 3D-printing processes on account of flow cessation in the extruder, and filament fragility/rupture following deposition. Understanding the nature of rheological changes that occur is critical to manipulate flow conditions or to dose flow modifiers for 3D-printing. Therefore, this paper elucidates the influences of clay particulates on controlling flow cessation and the shape stability of dense cementing suspensions that typically feature poor printability. A rope coiling method was implemented with varying stand-off distances to probe the buckling stability and tendency to fracture of dense suspensions that undergo stretching and bending during deposition. The contributions of flocculation and short-term percolation due to the kinetics of structure formation to deformation rate were deconvoluted using a stepped isostress method. It is shown that the shear stress indicates a divergence with a power-law scaling when the particle volume fraction approaches the jamming limit; φ → φj ≈ φmax. Such a power-law divergence of the shear stress decreases by a factor of 10 with increasing clay dosage. Such behavior in clay-containing suspensions arises from a decrease in the relative packing fraction (φ/φmax) and the formation of fractally-architected aggregates with stronger interparticle interactions, whose uniform arrangement controls flow cessation in the extruder and suspension homogeneity, thereby imparting greater buckling stability. The outcomes offer new insights for assessing/improving the extrudability and printability behavior during slurry-based 3D-printing process.

Dispersing nano- and micro-sized portlandite particulates via electrosteric exclusion at short screening lengths

In spite of their high surface charge (zeta potential ζ = +34 mV), aqueous suspensions of portlandite (calcium hydroxide: Ca(OH)2) exhibit a strong tendency to aggregate, and thereby present unstable suspensions. While a variety of commercial dispersants seek to modify the suspension stability and rheology (e.g., yield stress, viscosity), it remains unclear how the performance of electrostatically and/or electrosterically based additives is affected in aqueous environments having either a high ionic strength and/or a pH close to the particle's isoelectric point (IEP). We show that the high native ionic strength (pH ≈ 12.6, IEP: pH ≈ 13) of saturated portlandite suspensions strongly screens electrostatic forces (Debye length: κ-1 = 1.2 nm). As a result, coulombic repulsion alone is insufficient to mitigate particle aggregation and affect rheology. However, a longer-range geometrical particle-particle exclusion that arises from electrosteric hindrance caused by the introduction of comb polyelectrolyte dispersants is very effective at altering the rheological properties and fractal structuring of suspensions. As a result, comb-like dispersants that stretch into the solvent reduce the suspension's yield stress by 5× at similar levels of adsorption as compared to linear dispersants, thus enhancing the critical solid loading (i.e., at which jamming occurs) by 1.4×. Significantly, the behavior of diverse dispersants is found to be inherently related to the thickness of the adsorbed polymer layer on particle surfaces. These outcomes inform the design of dispersants for concentrated suspensions that present strong charge screening behavior.

Breaking the Limit of Micro-Ductility in Oxide Glasses

Oxide glasses are one of the most important engineering and functional material families owing to their unique features, such as tailorable physical properties. However, at the same time intrinsic brittleness has been their main drawback, which severely restricts many applications. Despite much progress, a breakthrough in developing ultra-damage-resistant and ductile oxide glasses still needs to be made. Here, a critical advancement toward such oxide glasses is presented. In detail, a bulk oxide glass with a record-high crack resistance is obtained by subjecting a caesium aluminoborate glass to surface aging under humid conditions, enabling it to sustain sharp contact deformations under loads of ≈500 N without forming any strength-limiting cracks. This ultra-high crack resistance exceeds that of the annealed oxide glasses by more than one order of magnitude, making this glass micro-ductile. In addition, a remarkable indentation behavior, i.e., a time-dependent shrinkage of the indent cavity, is demonstrated. Based on structural analyses, a molecular-scale deformation model to account for both the ultra-high crack resistance and the time-dependent shrinkage in the studied glass is proposed.

Topological Origins of the Mixed Alkali Effect in Glass

The mixed alkali effect, the deviation from expected linear property changes when alkali ions are mixed in a glass, remains a point of contention in the glass community. While several earlier models have been proposed to explain mixed alkali effects on ionic motion, models based on or containing discussion of structural aspects of mixed-alkali glasses remain rare by comparison. However, the transition-range viscosity depression effect is many orders in magnitude for mixed-alkali glasses, and the original observation of the effect (then known as the Thermometer Effect) concerned the highly anomalous temperature dependence of stress and structural relaxation time constants. With this in mind, a new structural model based on topological constraint theory is proposed herein which elucidates the origin of the mixed alkali effect as a consequence of network strain due to differing cation radii. Discussion of literature models and data alongside new molecular dynamics simulations and experimental data are presented in support of the model, with good agreement.

Revisiting the Dependence of Poisson's Ratio on Liquid Fragility and Atomic Packing Density in Oxide Glasses

Poisson's ratio (ν) defines a material's propensity to laterally expand upon compression, or laterally shrink upon tension for non-auxetic materials. This fundamental metric has traditionally, in some fields, been assumed to be a material-independent constant, but it is clear that it varies with composition across glasses, ceramics, metals, and polymers. The intrinsically elastic metric has also been suggested to control a range of properties, even beyond the linear-elastic regime. Notably, metallic glasses show a striking brittle-to-ductile (BTD) transition for ν-values above ~0.32. The BTD transition has also been suggested to be valid for oxide glasses, but, unfortunately, direct prediction of Poisson's ratio from chemical composition remains challenging. With the long-term goal to discover such high-ν oxide glasses, we here revisit whether previously proposed relationships between Poisson's ratio and liquid fragility (m) and atomic packing density (Cg) hold for oxide glasses, since this would enable m and Cg to be used as surrogates for ν. To do so, we have performed an extensive literature review and synthesized new oxide glasses within the zinc borate and aluminoborate families that are found to exhibit high Poisson's ratio values up to ~0.34. We are not able to unequivocally confirm the universality of the Novikov-Sokolov correlation between ν and m and that between ν and Cg for oxide glass-formers, nor for the organic, ionic, chalcogenide, halogenide, or metallic glasses. Despite significant scatter, we do, however, observe an overall increase in ν with increasing m and Cg, but it is clear that additional structural details besides m or Cg are needed to predict and understand the composition dependence of Poisson's ratio. Finally, we also infer from literature data that, in addition to high ν, high Young's modulus is also needed to obtain glasses with high fracture toughness.

Modifier clustering and avoidance principle in borosilicate glasses: A molecular dynamics study

Oxide glasses are typically described as having a random, disordered skeleton of network-forming polyhedra that are depolymerized by network-modifying cations. However, the existence of local heterogeneity or clustering within the network-forming and network-modifying species remains unclear. Here, based on molecular dynamics simulations, we investigate the atomic structure of a series of borosilicate glasses. We show that the network-modifying cations exhibit some level of clustering that depends on composition-in agreement with Greaves' modified random network model. In addition, we demonstrate the existence of some mutual avoidance among network-forming atoms, which echoes the Loewenstein avoidance principle typically observed in aluminosilicate phases. Importantly, we demonstrate that the degree of heterogeneity in the spatial distribution of the network modifiers is controlled by the level of ordering in the interconnectivity of the network formers. Specifically, the mutual avoidance of network formers is found to decrease the propensity for modifier clustering.

New insights into the atomic structure of amorphous TiO2 using tight-binding molecular dynamics

Amorphous TiO₂ (a-TiO₂) could offer an attractive alternative to conventional crystalline TiO₂ phases for photocatalytic applications. However, the atomic structure of a-TiO₂ remains poorly understood with respect to that of its crystalline counterparts. Here, we conduct some classical molecular dynamics simulations of a-TiO₂ based on a selection of empirical potentials. We show that, on account of its ability to dynamically assign the charge of each atom based on its local environment, the second-moment tight-binding charge equilibration potential yields an unprecedented agreement with available experimental data. Based on these simulations, we investigate the degree of order and disorder in a-TiO₂. Overall, the results suggest that a-TiO₂ features a large flexibility in its local topology, which may explain the high sensitivity of its structure to the synthesis method being used.

Anomalous variations in the viscous activation energy of suspensions induced by fractal structuring

HYPOTHESIS

In suspensions, the activation energy of viscous flow is an important property that controls the temperature dependence of the viscosity. However, the differentiated roles of the properties of the liquid phase and the structure of the solid particles in controlling the activation energy remain unclear. We propose here that particle fractal structuring yields an anomalous behavior in the activation energy of viscous flow.

EXPERIMENTS

The rheology of two series of suspensions consisting of glass beads suspended in poly(1-decene) was investigated over a wide range of solid volume fractions (0.00 ≤ φ ≤ 0.55). These suspensions were characterized by their viscosity (η, Pa∙s) via shear rate sweeps and by their yield stress (Pa) via oscillatory amplitude sweeps.

FINDINGS

Interestingly, for suspensions consisting of nominally smaller particles (d₅₀ ≈ 5 µm), we observe an anomalous decrease in the activation energy (E_a, kJ/mol) of viscous flow with increasing solid fraction. Based on oscillatory rheology analyses, it is suggested that such anomalous behavior arises due to entropic effects that result from the formation of fractally-architected cooperatively rearranging regions (i.e., agglomerates) in the suspension.

New insights into the sol-gel condensation of silica by reactive molecular dynamics simulations

The sol-gel method is an attractive technique to synthesize homogeneous silicate glasses with high purity while relying on a lower synthesis temperature than in the melt-quench method. However, the mechanism and kinetics of the condensation of the silicate network in aqueous solution remain unclear. Here, based on reactive molecular dynamics simulations (ReaxFF), we investigate the sol-gel condensation kinetics of a silica glass. The influence of the potential parametrization and system size is assessed. Our simulation methodology is found to offer good agreement with experiments. We show that the aqueous concentration of the Si(OH)₄ precursors and the local degree of polymerization of the Si atoms play a crucial role in controlling the condensation activation energy. Based on our simulations, we demonstrate that the gelation reaction is driven by the existence of some local atomic stress that gets released upon condensation.

Structural Compromise between High Hardness and Crack Resistance in Aluminoborate Glasses

Alkali aluminoborate glasses have recently been shown to exhibit a high threshold for indentation cracking compared to other bulk oxide glasses. However, to enable the use of these materials in engineering applications, there is a need to improve their hardness by tuning the chemical composition. In this study, we substitute alkaline earth for alkali network-modifying species at fixed aluminoborate base glass composition and correlate it with changes in the structure, mechanical properties, and densification behavior. We find that the increase in field strength (i.e., the charge-to-size ratio) achieved by substituting alkaline earth oxide from BaO to MgO manifests itself in a monotonic increase in several properties, such as atomic packing density, glass-transition temperature, densification ability, indentation hardness, and crack resistance. Although the use of alkaline earth oxides as modifier enables higher hardness values (increasing from 2.0 GPa for Cs to 5.8 GPa for Mg), their crack resistance is generally lower than that of the corresponding alkali aluminoborate glasses. We discuss the origin of this compromise between hardness and crack resistance in terms of the ability of the glass networks to undergo structural transformations and self-adapt under stress. We show that the extent of volume densification scales linearly with the number of pressure-induced coordination number changes of B and Al.

Direct observation of pitting corrosion evolutions on carbon steel surfaces at the nano-to-micro- scales

The Cl⁻-induced corrosion of metals and alloys is of relevance to a wide range of engineered materials, structures, and systems. Because of the challenges in studying pitting corrosion in a quantitative and statistically significant manner, its kinetics remain poorly understood. Herein, by direct, nano- to micro-scale observations using vertical scanning interferometry (VSI), we examine the temporal evolution of pitting corrosion on AISI 1045 carbon steel over large surface areas in Cl⁻-free, and Cl⁻-enriched solutions. Special focus is paid to examine the nucleation and growth of pits, and the associated formation of roughened regions on steel surfaces. By statistical analysis of hundreds of individual pits, three stages of pitting corrosion, namely, induction, propagation, and saturation, are quantitatively distinguished. By quantifying the kinetics of these processes, we contextualize our current understanding of electrochemical corrosion within a framework that considers spatial dynamics and morphology evolutions. In the presence of Cl⁻ ions, corrosion is highly accelerated due to multiple autocatalytic factors including destabilization of protective surface oxide films and preservation of aggressive microenvironments within the pits, both of which promote continued pit nucleation and growth. These findings offer new insights into predicting and modeling steel corrosion processes in mid-pH aqueous environments.

Rate controls on silicate dissolution in cementitious environments

The dissolution rate of silicate minerals and glasses in alkaline environments is of importance in cementitious systems due to its influences on: (a) early-age reactivity that affects the rate of strength gain and microstructure formation, and/or, (b) chemical durability of aggregates; compromises in which can result deleterious processes such as alkali-silica reaction (ASR). In spite of decades of study, quantitative linkages between the atomic structure of silicates and their dissolution rate in aqueous media (i.e., chemical reactivity) has remained elusive. Recently, via pioneering applications of molecular dynamics simulations and nanoscale-resolved measurements of dissolution rates using vertical scanning interferometry, a quantitative basis has been established to link silicate dissolution rates to the topology (rigidity) of their atomic networks. Specifically, an Arrhenius-like expression is noted to capture the dependence between silicate dissolution rates and the average number of constraints placed on a central atom in a network (nc, i.e., an indicator of the network’s rigidity). This finding is demonstrated by: (i) ordering fly ashes spanning Ca-rich/poor variants in terms of their reactivity, and, (ii) assessing alterations in the reactivity of albite, and quartz following irradiation due to their potential to induce ASR in concrete exposed to radiation, e.g., in nuclear power plants.       

Revealing the Effect of Irradiation on Cement Hydrates: Evidence of a Topological Self-Organization

Despite the crucial role of concrete in the construction of nuclear power plants, the effects of radiation exposure (i.e., in the form of neutrons) on the calcium-silicate-hydrate (C-S-H, i.e., the glue of concrete) remain largely unknown. Using molecular dynamics simulations, we systematically investigate the effects of irradiation on the structure of C-S-H across a range of compositions. Expectedly, although C-S-H is more resistant to irradiation than typical crystalline silicates, such as quartz, we observe that radiation exposure affects C-S-H's structural order, silicate mean chain length, and the amount of molecular water that is present in the atomic network. By topological analysis, we show that these "structural effects" arise from a self-organization of the atomic network of C-S-H upon irradiation. This topological self-organization is driven by the (initial) presence of atomic eigenstress in the C-S-H network and is facilitated by the presence of water in the network. Overall, we show that C-S-H exhibits an optimal resistance to radiation damage when its atomic network is isostatic (at Ca/Si = 1.5). Such an improved understanding of the response of C-S-H to irradiation can pave the way to the design of durable concrete for radiation applications.