Abstract 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.

Our multi-task neural network approach simultaneously predicts the concentration of all types of rare earth elements (REEs) in coal ashes, with an improved accuracy and robustness as compared to conventional single-task neural networks.

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.

Amorphous materials can exhibit varying degrees of nanoscale ductility depending on their atomic structure. Despite its critical importance for applications, the physical origin that controls ductility remains largely un-known. Here, by using molecular dynamics simulations, we investigate the ductile-to-brittle transition of oxide glasses as a function of the connectivity of the atomic network. Interestingly, based on topological constraint theory, we show that the structural origin of the ductile-to-brittle transition is the rigidity transition caused by the percolation of stressed-rigid atomic clusters. Our further analysis of four-point correlation functions reveals that, similar to the case of supercooled liquids, the plastic dynamics of oxide glasses at room temperature are strongly correlated and spatially heterogeneous. Surprisingly, the dynamical length scale of plastic events significantly decreases when the stressed-rigid cluster percolates, resulting in a narrower transient plastic rear-rangement region. These results provide physical insights into the relationship between the topological features of atomic structure, fracture behavior, and stress-induced dynamical heterogeneity of glasses.

Atomic structure determines physical properties, but for glassy materials, the nature of structure-property relationships remains ambiguous. Since glass properties are governed by both chemistry and structure, it is difficult to dissociate these two effects. Here, the sole effect of the structure on property is isolated by treating an industrial aluminosilicate glass with either thermal-annealing or pressure-quenching processes to produce glasses with varying densities and hardnesses (at constant composition). To explore the underlying structural origin of property changes, neutron total-scattering patterns of these glasses were measured. These results confirm the applicability of rigid-unit mode theory since the short-range tetrahedra were found to remain unaffected. In contrast, close correlations are derived between properties and medium-range structure (as encoded in various features of the first sharp diffraction peak). Overall, it reveals that the increase in the medium-range order is the structural origin of the extra extent of hardness increase beyond the densification effects.

Developing new sustainable concrete technology has become an urgent need, requiring faster and deeper insights into the fundamental mechanisms driving the cement hydration reactions. Molecular simulations have the potential to provide such understanding since the hydration reaction and the cement chemistry are particularly dominated by mechanisms at the atomic scale. In this letter, we review the application of two major approaches namely classical (including reactive) molecular dynamics simulations and density function theory calculations of cementitious materials. We give an overview of molecular simulations involving the major mineral and hydrate phases.

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 from the coal combustion at electric plants is commonly used for partially replacing portland cement in concrete production. Because of the varying nature of the coal source and the different processing protocols, different fly ashes exhibit wide ranges of physical and chemical characteristics, resulting in distinct impacts on concrete strength. Thus far, the most adopted method for assessing a given fly ash is specified by ASTM C618, Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete, wherein a series of influential chemical and physical features can be correlated to fly ash's strength activity index (SAI). However, limited knowledge is available on how exactly the individual material attribute affects SAI, so accurately predicting the SAI remains out of reach. Here, we take advantage of recent advances in machine learning to reveal the origins of fly ash's SAI. Leveraging a data set comprising 2,158 fly ash samples, we trained neural network models to predict 28-day SAI based on the sole knowledge of ASTM C618 material attributes. The results demonstrate that SAI is a complex property that does not systematically follow the conventional Class C/F classification. To gain a deeper insight into this matter, we further quantify the influence of each attribute on SAI as captured by the machine learning model.

When subjected to fires, cementitious composites can be seriously damaged. However, the mechanical behavior of nanoscale calcium silicate hydrate (C–S–H) grains, which are the main binder of cementitious composites, exposed to elevated temperatures under shear deformations remain poorly investigated. In this paper, considering different calcium/silicate (C/S) molar ratios (i.e., C/S = 1.10, 1.33, and 1.64), the shear behavior of the C–S–H grain after exposure to different high-temperature levels (i.e., 300 K, 500 K, 700 K, 900 K, and 1000 K) is studied by conducting series of reactive molecular dynamics simulations. Results reveal that the C–S–H grain exhibits good plasticity under shear deformation. Furthermore, the shear modulus of the C–S–H grain is between 14 GPa and 17 GPa and exhibits a decrease with the calcium/silicon (C/S) molar ratio at ambient temperature. While the shear strength is around 1.0 GPa and reaches the lowest value at C/S = 1.64. Interestingly, we report that heating can lead to the increase of the shear modulus and shear strength due to the evaporation of the interlayer water which generally acts as the lubricant. Additionally, we show that heating has no clear influence on the shear strain corresponding to the onset of yielding when the C/S ratio is high but can indeed improve the shear strain corresponding to the shear strength. Furthermore, the yielding area of the C–S–H grain under the shear deformation can be enlarged. Finally, factors affecting the strength degradation of cementitious composites after being heated to different temperature levels are further discussed based on the simulation results.

Despite the ubiquity of porous materials, their mechanical behaviors (e.g., fracture) remain only partially understood. Here, we propose a novel analytical stochastic micromechanical damage model to describe the fracture of porous materials subjected to uniaxial tension. This analytical model relies on parallel elastic and plastic elements to describe the nonlinear stress-strain curve of porous phases. We then develop a stochastic damage model to describe the propagation of randomly scattered voids or microflaws. This model allows us to identify the key influential features that govern the failure of porous materials. Finally, we demonstrate the accuracy of our model by validating its outcomes by a series of peridynamic simulations.

Acoustic stimulation offers a green pathway for the extraction of valuable elements such as Si, Ca, and Mg via solubilization of minerals and industrial waste materials. Prior studies have focused on the use of ultrasonic frequencies (20-40 kHz) to stimulate dissolution, but megasonic frequencies (>= 1 MHz) offer benefits such as matching of the resonance frequencies of solute particles and an increased frequency of cavitation events. Here, based on dissolution tests of a series of minerals, it is found that dissolution under resonance conditions produced dissolution enhancements between 4x-to-6x in Si-rich materials (obsidian, albite, and quartz). Cavitational collapse induced by ultrasonic stimulation was more effective for Ca- and Mg-rich carbonate precursors (calcite and dolomite), exhibiting a significant increase in the dissolution rate as the particle size was reduced (i.e. available surface area was increased), resulting in up to a 70x increase in the dissolution rate of calcite when compared to unstimulated dissolution for particles with d(50) < 100 mu m. Cavitational collapse induced by megasonic stimulation caused a greater dissolution enhancement than ultrasonic stimulation (1.5x vs 1.3x) for amorphous class F fly ash, despite its higher Si content because the hollow particle structure was susceptible to breakage by the rapid and high number of lower-energy megasonic cavitation events. These results are consistent with the cavitational collapse energy following a normal distribution of energy release, with more cavitation events possessing sufficient energy to break Ca-O and Mg-O bonds than Si-O bonds, the latter of which has a bond energy approximately double the others. The effectiveness of ultrasonic dissolution enhancement increased exponentially with decreasing stacking fault energy (i.e., resistance to the creation of surface faults such as pits and dislocations), while, in turn, the effectiveness of megasonic dissolution increased linearly with the stacking fault energy. These results give new insights into the use of acoustic frequency selections for accelerating elemental release from solutes by the use of acoustic perturbation.

Nanoindentation is a widely used method to probe the mechanical properties of glasses. However, interpreting glasses' response to nanoindentation can be challenging due to the complex nature of the stress field under the indenter tip and the lack of in situ characterization techniques. Here, we present a numerical model describing the nanoindentation of an archetypical soda-lime silicate window glass by means of peridynamic simulations. We show that, although it does not capture shear flow and permanent densification, peridynamics exhibits a good agreement with experimental nanoindentation data and offers a direct access to the stress field forming under the indenter tip.

The moisture content in cement pastes influences their mechanical properties and durability. However, the complex, multiscale nature of cement pastes makes it challenging to isolate the contributions of each scale to their macroscopic water sorption isotherms. In particular, the contribution of the calcium-silicate-hydrate gel (the binding phase of cement pastes) remains only partially understood. Here, we introduce a density functional theory lattice model describing water sorption in calcium-silicate-hydrate, which properly reproduces experimental water sorption isotherms in cement pastes. Based on this model, we deconstruct the contribution of each pore scale (interlayer spacing, gel pores, and capillary pores) to the total sorption isotherm. We show that, when the relative humidity is below 80%, the calcium-silicate-hydrate gel accounts for more than 90% of the moisture content adsorbed in cement pastes. In turn, we find that the contribution of the interlayer space within the calcium-silicate-hydrate grains is governed by the competition between the rate of interlayer space opening and the increasing propensity for water to fill larger pores upon increasing relative humidity. Overall, our results highlight the key role played by the calcium-silicate-hydrate in governing the sorption isotherms of cement pastes.

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.

Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition-property relationships of glasses and other materials with complex structures. A series of 20 borosilicate, aluminosilicate, and boroaluminosilicate glasses have been modeled using MD simulations with recently developed effective potentials. Short- and medium-range structures of these glasses were analyzed and, based on the structural information, QSPR analysis of the initial dissolution rates (r(0)) of these glasses that were measured at 90 degrees C and pH 9 by using various structural descriptors such as percentage of bridging oxygen species, network connectivity, and average ring size. The structural descriptor, F-net, which contains both energetic information such as single bond strength and structural information such as cation coordination number and Q(n) distribution, was also used. It was found that while the overall network connectivity, average ring size and F-net provide reasonable correlations with r(0) of studied glasses, F-net gives the best correlation among the descriptors. For glasses that show incongruent dissolution, it was found that modification of glass compositions to account for preferential release of modifier cations is necessary to achieve best correlations. The findings were discussed with results of recent studies on evaluating the compositional dependence of glass dissolution behavior using the topological-constraints-based models.

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 disordering of the atomic network of minerals typically results in swelling of the structure. However, the origin of such expansion and the extent thereof remain unclear. Here, we conduct a series of molecular dynamics simulations of vitrification- and irradiation-induced disordering to reveal the nature of disorder-induced expansion in silicate phases. We show that pristine crystals are topologically overconstrained—i.e., some interatomic constraints are mutually redundant. Consequently, the weaker atomic constraints yield to the stronger ones— a behavior that manifests itself through some internal elastic stress in the network. Interestingly, we demonstrate that disordering results in a release of this internal stress by breaking the weaker constraints. The release of such internal strain explains the macroscopic expansion exhibited by atomic networks upon disordering.

Although calcium carbonate (CaCO3) precipitation plays an important role in nature, its mechanism remains only partially understood. Further understanding the atomic driving force behind the CaCO3 precipitation could be key to facilitate the capture, immobilization, and utilization of CO2 by mineralization. Here, based on molecular dynamics simulations, we investigate the mechanism of the early-stage nucleation of an amorphous calcium carbonate gel. We show that the gelation reaction manifests itself by the formation of some calcium carbonate clusters that grow over time. Interestingly, we demonstrate that the gelation reaction is driven by the existence of some competing local molecular stresses within the Ca and C precursors, which progressively get released upon gelation. This internal molecular stress is found to originate from the significantly different local coordination environments exhibited by Ca and C atoms. These results highlight the key role played by the local stress acting within the atomic network in governing gelation reactions.