Cooling rate and stress relaxation in silica melts and glasses via microsecond molecular dynamics

Large-scale atomistic study of plasticity in amorphous gallium oxide with ab-initio accuracy

Compared to the widely investigated crystalline polymorphs of gallium oxide ([Formula: see text]), knowledge about its amorphous state is very limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate the formation and plastic behavior of amorphous [Formula: see text] (a-[Formula: see text]). Amorphization of gallium oxide melt is successfully observed at ultrahigh cooling rates, including a distinct glass transition. The glass transition temperature is evaluated to range from 1234 to 1348 K at different cooling rates. Structural analysis shows similarities between a-[Formula: see text] and amorphous alumina (a-[Formula: see text]) in many aspects, including pair distribution function, coordination distribution, and bond angle distribution. In the tension simulations, highly plastic behavior at room temperature is observed, highly comparable to a-[Formula: see text]. Based on multiple quantitative characterization results, we show that a-[Formula: see text] exhibits a higher nucleation rate of localized plastic strain events compared to a-[Formula: see text], which can increase the material's resistance to shear banding formation during deformation.

Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials

Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.g., MAP). Examples are drawn from amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length-scales accessible through numerical simulations.

Learning Adsorption Patterns on Amorphous Surfaces

The physicochemical heterogeneity found on amorphous surfaces leads to a complex interaction of adsorbate molecules with topological and undercoordinated defects, which enhance the adsorption capacity and can participate in catalytic reactions. The identification and analysis of the adsorption structure observed on amorphous surfaces require novel tools that allow the segmentation of the surfaces into complex-shaped regions that contrast with the periodic patterns found on crystalline surfaces. We propose a Random Forest (RF) classifier that segments the surface into regions that can then be further analyzed and classified to reveal the dynamics of the interaction with the adsorbate. The RF segmentation is applied to the surface density map of the adsorbed molecules and employs multiple features (intensity, gradient, and the eigenvalues of the Hessian matrix) which are nonlocal and allow a better identification of the adsorption structures. The segmentation depends on a set of parameters that specify the training set and can be tailored to serve the specific purpose of the segmentation. Here, we consider an example in which we aim to separate highly heterogeneous regions from weakly heterogeneous regions. We demonstrate that the RF segmentation is able to separate the surface into a fully connected weakly heterogeneous region (whose behavior is somehow similar to crystalline surfaces and has an exponential distribution of the residence time) and a very heterogeneous region characterized by a complex residence-time distribution, which is generated by the undercoordinated defects and is responsible for the peculiar characteristics of the amorphous surface.

Melting Point and Crystal Growth Kinetics of Metals and Metal Oxides Using Reactive Force Fields: The Case of Aluminum and Alumina

Alumina and aluminum are strategic materials employed in energy applications, with metal aluminum being interesting in phase change material applications. Therefore, the theoretical description of the thermophysical properties of these materials represents an important objective. Here, we investigate the liquid-solid coexistence properties of aluminum and alumina using a state-of-the-art reactive force field (ReaxFF) and molecular dynamics simulations. Aluminum features ultrafast crystal growth, which enables the direct determination of its melting temperature via direct coexistence simulations (858 ± 2 K). However, at standard pressure, alumina is easily trapped in a glass state, preventing the application of the direct coexistence method. We demonstrate that direct coexistence can be used at high pressures above 2 GPa, where alumina features a higher melting temperature, and the liquid-solid interface exhibits enhanced dynamics. Our approach opens a route to obtain the melting temperature of ReaxFF alumina at standard pressure (1670 ± 10 K) and, more generally, a viable method for calculating the melting point of metal oxides via direct coexistence simulations. We further investigated the dynamics of crystal growth of the solid-liquid aluminum and alumina interfaces.

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.

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [Wang, H. Comput. Phys. Commun. 2018, 228, 178-184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.

Review: understanding the properties of amorphous materials with high-performance computing methods

Amorphous materials have no long-range order in their atomic structure. This makes much of the formalism for the study of crystalline materials irrelevant, and so elucidating their structure and properties is challenging. The use of computational methods is a powerful complement to experimental studies, and in this paper we review the use of high-performance computing methods in the simulation of amorphous materials. Five case studies are presented to showcase the wide range of materials and computational methods available to practitioners in this field. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced 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.

Structural Ordering in SWCNT-Polyimide Nanocomposites and Its Influence on Their Mechanical Properties

Using fully-atomistic models, tens-microseconds-long molecular-dynamic modelling was carried out for the first time to simulate the kinetics of polyimides ordering induced by the presence of single-walled carbon nanotube (SWCNT) nanofillers. Three polyimides (PI) were considered with different dianhydride fragments, namely 3,3',4,4'-biphenyltetracarboxylic dianhydride (BPDA), 2,3',3,4'-biphenyltetracarboxylic dianhydride (aBPDA), and 3,3',4,4'-oxidiphthalic dianhydride (ODPA) and same diamine 1,4-bis[4-(4-aminophenoxy)phenoxy]benzene (diamine P3). Both crystallizable PI BPDA-P3 and two amorphous polyimides ODPA-P3 and aBPDA-P3 reinforced by SWCNTs were studied. The structural properties of the nanocomposites at temperature close to the bulk polymer melting point were studied. The mechanical properties were determined for the nanocomposites cooled down to the glassy state. It was found that the SWCNT nanofiller initiates' structural ordering not only in the crystallizable BPDA-P3 but also in the amorphous ODPA-P3 samples were in agreement with previously obtained experimental results. Two stages of the structural ordering were detected in the presence of SWCNTs, namely the orientation of the planar moieties followed by the elongation of whole polymer chains. The first type of local ordering was observed on the microsecond time scale and did not lead to the change of the mechanical properties of a polymer binder in considered nanocomposites. At the end of the second stage, both BPDA-P3 and ODPA-P3 PI chains extended completely along the SWCNT surface, which in turn led to enhanced mechanical characteristics in their glassy state.

A mathematical model for fitting and predicting relaxation modulus and simulating viscoelastic responses

We propose a mathematical model for relaxation modulus and its numerical solution. The model formula is extended from sigmoidal function considering nonlinear strain hardening. Its physical meaning can be interpreted by a macroscale elastic network-viscous medium model with only five model parameters in a simpler format than the molecular-chain-based polymer models to represent general solid materials. We also developed a finite-element (FE) framework and robust numerical algorithm to implement this model for simulating responses under both static and dynamic loadings. We validated the model through both experimental data and numerical simulations on a variety of materials including asphalt concrete, polymer, spider silk, hydrogel, agar and bone. By satisfying the second law of thermodynamics in the form of Calusius-Duhem inequality, the model is able to simulate creep and sinusoidal deformation as well as energy dissipation. Compared to the Prony series, the widely used model with a large number of model parameters, the proposed model has improved accuracy in fitting experimental data and prediction stability outside of the experimental range with competitive numerical stability and computation speed. We also present simulation results of nonlinear stress-strain relationships of spider silk and hydrogels, and dynamic responses of a multilayer structure.

B2O3/SiO2 substitution effect on structure and properties of Na2O–CaO–SrO–P2O5–SiO2 bioactive glasses from molecular dynamics simulations

The effect of B₂O₃/SiO₂ substitution in SrO-containing 55S4.3 bioactive glasses on glass structure and properties, such as ionic diffusion and glass transition temperature, was investigated by combining experiments and molecular dynamics simulations with newly developed potentials.

Universal scaling in the aging of the strong glass former SiO2

We show that the aging dynamics of a strong glass former displays a strikingly simple scaling behavior, connecting the average dynamics with its fluctuations, namely, the dynamical heterogeneities. We perform molecular dynamics simulations of SiO2 with van Beest-Kramer-van Santen interactions, quenching the system from high to low temperature, and study the evolution of the system as a function of the waiting time tw measured from the instant of the quench. We find that both the aging behavior of the dynamic susceptibility χ4 and the aging behavior of the probability distribution P(fs,r) of the local incoherent intermediate scattering function fs,r can be described by simple scaling forms in terms of the global incoherent intermediate scattering function C. The scaling forms are the same that have been found to describe the aging of several fragile glass formers and that, in the case of P(fs,r), have been also predicted theoretically. A thorough study of the length scales involved highlights the importance of intermediate length scales. We also analyze directly the scaling dependence on particle type and on wavevector q and find that both the average and the fluctuations of the slow aging dynamics are controlled by a unique aging clock, which is not only independent of the wavevector q, but is also the same for O and Si atoms.