Plastic flow in metallic glasses (MGs) is known to be mediated by shear transformations
(STs), which have been hypothesized to preferentially initiate from identifiable local “defect” …

We consider unsupervised learning methods for characterizing the disordered microscopic
structure of supercooled liquids and glasses. Specifically, we perform dimensionality …

The use of machine learning to develop neural network potentials (NNP) representing the
interatomic potential energy surface allows us to achieve an optimal balance between …

The fast increase in available computation power allowed us to decrease the cooling rate in
molecular dynamics (MD) simulation of vitrification by several orders of magnitude. While the …

A brief overview of the key methods for analyzing the structure of three-dimensional (3D)
matter is given by using as an example melts of simple model systems like Lennard-Jones …

It has so far remained a major challenge to quantitatively predict the boson peak, a THz
vibrational anomaly universal for glasses, from features in the amorphous structure. Using …

The structural origin of the slow dynamics in glass formation remains to be understood
owing to the subtle structural differences between the liquid and glass states. Even from …

Understanding the mechanisms which relate properties of liquid and solid phases is crucial
for fabricating new advanced solid materials, such as glasses, quasicrystals and high …

Understanding the mechanisms by which crystal nuclei form is crucial for many phenomena
such as gaining control over crystallization in glass-forming materials or accurately modeling …

Few general models representing certain classes of real glass-forming systems play a
special role in computer simulations of supercooled liquid and glasses. Recently, it was …