The global demand for data storage and processing has increased exponentially in recent
decades. To respond to this demand, research efforts have been devoted to the …

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error”
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …

Atomic structure dictates the performance of all materials systems; the characteristic of
disordered materials is the significance of spatial and temporal fluctuations on composition …

The application of machine learning to predict materials' properties usually requires a large
number of consistent data for training. However, experimental datasets of high quality are …

Fused silica glass, with its large transparency range, low thermal expansion, high resistance
to optical damage, high refractive index homogeneity, and ease of finishing has found use …

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to
determine a polarizable force field parameters using only ab initio data from quantum …

Borosilicate glasses are traditionally challenging to model using atomic scale simulations
due to the composition and thermal history dependence of the coordination state of B atoms …

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 …

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of
complex molecular events. However, these simulations can rarely sample the required time …

It is known that the optical transparency of an oxide glass decreases with an increase in the
size and fraction of crystals in the glass during heat‐treatment. Here, the authors report an …