The energy landscape governs ductility in disordered materials

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.

Structural Causes of Brittleness Changes in Aluminosilicate Glasses with Different Cooling Rates

Numerous sources have already demonstrated that varying annealing rates can result in distinct toughness and brittleness in glass. To determine the underlying mechanisms driving this phenomenon, molecular dynamic (MD) simulations were employed to investigate the microstructure of aluminosilicate glasses under different cooling rates, and then uniaxial stretching was performed on them under controlled conditions. Results indicated that compared with short-range structure, cooling rate has a greater influence on the medium-range structure in glass, and it remarkably affects the volume of voids. Both factors play a crucial role in determining the brittleness of the glass. The former adjusts network connectivity to influence force transmission by manipulating the levels of bridging oxygen (BO) and non-bridging oxygen (NBO), and the latter accomplishes the objective of influencing brittleness by modifying the environmental conditions that affect the changes in BO and NBO content. The variation in the void environment results in differences in the strategies of the changes in BO and NBO content during glass stress. These findings stem from the excellent response of BO and NBO to the characteristic points of stress-strain curves during stretching. This paper holds importance in understanding the reasons behind the effect of cooling rates on glass brittleness and in enhancing our understanding of the ductile/brittle transition (DTB) in glass.

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.

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.

Nanoscale Heterogeneities of Non-Noble Iron-Based Metallic Glasses toward Efficient Water Oxidation at Industrial-Level Current Densities

Scaling up the production of cost-effective electrocatalysts for efficient water splitting at the industrial level is critically important to achieve carbon neutrality in our society. While noble-metal-based materials represent a high-performance benchmark with superb activities for hydrogen and oxygen evolution reactions, their high cost, poor scalability, and scarcity are major impediments to achieve widespread commercialization. Herein, a flexible freestanding Fe-based metallic glass (MG) with an atomic composition of Fe₅₀Ni₃₀P₁₃C₇ was prepared by a large-scale metallurgical technique that can be employed directly as a bifunctional electrode for water splitting. The surface hydroxylation process created unique structural and chemical heterogeneities in the presence of amorphous FeOOH and Ni₂P as well as nanocrystalline Ni₂P that offered various active sites to optimize each rate-determining step for water oxidation. The achieved overpotentials for the oxygen evolution reaction were 327 and 382 mV at high current densities of 100 and 500 mA cm^-2 in alkaline media, respectively, and a cell voltage of 1.59 V was obtained when using the MG as both the anode and the cathode for overall water splitting at a current density of 10 mA cm^-2. Theoretical calculations unveiled that amorphous FeOOH makes a significant contribution to water molecule adsorption and oxygen evolution processes, while the amorphous and nanocrystalline Ni₂P stabilize the free energy of hydrogen protons (ΔG_H*) in the hydrogen evolution process. This MG alloy design concept is expected to stimulate the discovery of many more high-performance catalytic materials that can be produced at an industrial scale with customized properties in the near future.

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.