There are unique benefits and challenges to glass modeling that differ from other areas of material modeling. This is due to the lack of dependence on temperature or pressure history and the statistical nature and availability of the glass-forming fluid. Nearly all components are found in the periodic table of glasses. There are many methods to shape glass. It is quite different to model crystalline materials. These are just a few of the methods that can be used to make glass.
glass molds employs empirical methods to combine them (ie data-driven machine learning, finite element fires, models for mechanical and/or acoustic property), Composition/property/processing relationships. Diffusion, statistical physics, and first principles, theories, as well as energy landscapes in quantum mechanics.
It is possible to model multiple orders of magnitudes at different lengths and time scales in large composition spaces. This would make it prohibitively expensive for experimental exploration.
Computational codes are crucial tools in the analysis and geochemical modeling of mineral alteration. They are capable of handling key mechanisms like dissolution, precipitation, and diffusion at various spatial and temporal resolutions.
It is important to describe the amorphous layer that forms on the glass surface during the corrosion patterning. It is important to describe its effect on the kinetics and behavior of the glass.
GRAAL (Glass Reactivity in Altered Layer Tolerance) allows for simple implementation passivation in a reactive transportation code. It provides information on the composition of an amorphous coating and its solubility.
The properties and size of the protective layer determine the rate of glass alteration. Passivation is a measure of how much the primary mineral dissolves. The thicker the protective layer, the lower its dissolution rate. You can use the model to measure parameters in simple glass alteration experiments.