62 research outputs found
Predicting the dissolution kinetics of silicate glasses using machine learning
Predicting the dissolution rates of silicate glasses in aqueous conditions is
a complex task as the underlying mechanism(s) remain poorly understood and the
dissolution kinetics can depend on a large number of intrinsic and extrinsic
factors. Here, we assess the potential of data-driven models based on machine
learning to predict the dissolution rates of various aluminosilicate glasses
exposed to a wide range of solution pH values, from acidic to caustic
conditions. Four classes of machine learning methods are investigated, namely,
linear regression, support vector machine regression, random forest, and
artificial neural network. We observe that, although linear methods all fail to
describe the dissolution kinetics, the artificial neural network approach
offers excellent predictions, thanks to its inherent ability to handle
non-linear data. Overall, we suggest that a more extensive use of machine
learning approaches could significantly accelerate the design of novel glasses
with tailored properties
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 = C3H3N2â) 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
Advancing the mechanical performance of glasses: Perspectives and challenges
Glasses are materials that lack a crystalline microstructure and longârange atomic order. Instead, they feature heterogeneity and disorder on superstructural scales, which have profound consequences for their elastic response, material strength, fracture toughness, and the characteristics of dynamic fracture. These structureâproperty relations present a rich field of study in fundamental glass physics and are also becoming increasingly important in the design of modern materials with improved mechanical performance. A first step in this direction involves glassâlike materials that retain optical transparency and the haptics of classical glass products, while overcoming the limitations of brittleness. Among these, novel types of oxide glasses, hybrid glasses, phaseâseparated glasses, and bioinspired glassâpolymer composites hold significant promise. Such materials are designed from the bottomâup, building on structureâproperty relations, modeling of stresses and strains at relevant length scales, and machine learning predictions. Their fabrication requires a more scientifically driven approach to materials design and processing, building on the physics of structural disorder and its consequences for structural rearrangements, defect initiation, and dynamic fracture in response to mechanical load. In this article, a perspective is provided on this highly interdisciplinary field of research in terms of its most recent challenges and opportunities.The mechanical performance of glassy materials presents a major challenge in modern glass science and technology. With a focus on visually transparent, inorganic and hybrid glasses, a perspective on the most recent developments in the field is provided herein, emphasizing the importance of translating fundamental insight from glass physics into future applications
Temperature-Modulated Differential Scanning Calorimetry Analysis of High-Temperature Silicate Glasses
Differential scanning calorimetry (DSC) is one of the most versatile probes
for silicate glasses, allowing determination of, e.g., transition temperatures
(glass, crystallization, melting) and the temperature dependence of heat
capacity. However, complications arise for glasses featuring overlapping
transitions and low sensitivity, e.g., arising from SiO2-rich compositions with
small change in heat capacity during glass transition or the low sensitivity of
thermocouples at high temperature. These challenges might be overcome using
temperature-modulated DSC (TM-DSC), which enables separation of overlapping
signals and improved sensitivity at the expense of increased measurement
duration
Cooling-Rate Effects in Sodium Silicate Glasses: Bridging the Gap between Molecular Dynamics Simulations and Experiments
Although molecular dynamics (MD) simulations are commonly used to predict the
structure and properties of glasses, they are intrinsically limited to short
time scales, necessitating the use of fast cooling rates. It is therefore
challenging to compare results from MD simulations to experimental results for
glasses cooled on typical laboratory time scales. Based on MD simulations of a
sodium silicate glass with varying cooling rate (from 0.01 to 100 K/ps), here
we show that thermal history primarily affects the medium-range order
structure, while the short-range order is largely unaffected over the range of
cooling rates simulated. This results in a decoupling between the enthalpy and
volume relaxation functions, where the enthalpy quickly plateaus as the cooling
rate decreases, whereas density exhibits a slower relaxation. Finally, we
demonstrate that the outcomes of MD simulations can be meaningfully compared to
experimental values if properly extrapolated to slower cooling rates
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