We introduce GlassMLP, a machine learning framework using physics-inspired structural
input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep …

The low-frequency vibrational and low-temperature thermal properties of amorphous solids
are markedly different from those of crystalline solids. This situation is counterintuitive …

The dynamics of a supercooled liquid near the glass transition is characterized by two-step
relaxation, fast β and slow α relaxations. Because of the apparently disordered nature of …

Dense active systems are widespread in nature, examples range from bacterial colonies to
biological tissues. Dense clusters of active particles can be obtained by increasing the …

We explore the emergence of nonequilibrium collective motion in disordered nonthermal
active matter when persistent motion and crowding effects compete, using simulations of a …

Phase transitions significantly differ between 2D and 3D systems, but the influence of
dimensionality on the glass transition is unresolved. We use microscopy to study colloidal …

The aim of this work is to present an overview and a critical discussion on two-dimensional
materials and functional nanostructures exhibiting ferromagnetic and antiferromagnetic long …

Glass transition is characterised by drastic dynamical slowing down upon cooling,
accompanied by growing spatial heterogeneity. Its rationalisation by subtle changes in the …

Supercooled liquids display sluggish dynamics, often attributed to their structural
characteristics, yet the underlying mechanism remains elusive. Here we conduct numerical …

We develop a machine learning model, which predicts structural relaxation from amorphous
supercooled liquid structures. The trained networks are able to predict dynamic …