Effects of temperature and CO2 concentration on the early stage nucleation of calcium carbonate by reactive molecular dynamics simulations.

It is significant to investigate the calcium carbonate (CaCO3) precipitation mechanism during the carbon capture process; nevertheless, CaCO3 precipitation is not clearly understood yet. Understanding the carbonation mechanism at the atomic level can contribute to the mineralization capture and utilization of carbon dioxide, as well as the development of new cementitious materials with high-performance. There are many factors, such as temperature and CO2 concentration, that can influence the carbonation reaction. In order to achieve better carbonation efficiency, the reaction conditions of carbonation should be fully verified. Therefore, based on molecular dynamics simulations, this paper investigates the atomic-scale mechanism of carbonation. We investigate the effect of carbonation factors, including temperature and concentration, on the kinetics of carbonation (polymerization rate and activation energy), the early nucleation of calcium carbonate, etc. Then, we analyze the local stresses of atoms to reveal the driving force of early stage carbonate nucleation and the reasons for the evolution of polymerization rate and activation energy. Results show that the higher the calcium concentration or temperature, the higher the polymerization rate of calcium carbonate. In addition, the activation energies of the carbonation reaction increase with the decrease in calcium concentrations.

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.

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.

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.

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.

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.

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.

Revealing the relationship between liquid fragility and medium-range order in silicate glasses.

Despite decades of studies, the nature of the glass transition remains elusive. In particular, the sharpness of the dynamical arrest of a melt at the glass transition is captured by its fragility. Here, we reveal that fragility is governed by the medium-range order structure. Based on neutron-diffraction data for a series of aluminosilicate glasses, we propose a measurable structural parameter that features a strong inverse correlation with fragility, namely, the average medium-range distance (MRD). We use in-situ high-temperature neutron-scattering data to discuss the physical origin of this correlation. We argue that glasses exhibiting low MRD values present an excess of small network rings. Such rings are unstable and deform more readily with changes in temperature, which tends to increase fragility. These results reveal that the sharpness of the dynamical arrest experienced by a silicate glass at the glass transition is surprisingly encoded into the stability of rings in its network.

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.

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.

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.

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.

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 m0.5) 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.