Related papers: Machine Learning for Phase Behavior in Active Matt…
The steady state and phase ordering kinetics in a pure active Borwnian particle system are studied in recent years. In binary mixture of active and passive Brownian particles passive particles are used as probe to understand the properties…
A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic…
Systems containing active components are intrinsically out of equilibrium, while binary mixtures reach their equilibrium configuration when complete phase separation is achieved. Active particles are found to stabilise non-equilibrium…
An important question in the field of active matter is whether or not it is possible to predict the phase behavior of these systems. Here, we study the phase coexistence of binary mixtures of torque-free active Brownian particles, for both…
We present the Brownian dynamics simulation of active colloidal suspension in two dimensions, where the self-propulsion speed of a colloid is regulated according to the local density sensed by it. The role of concentration-dependent…
We investigate the efficient learning of magnetic phases using artificial neural networks trained on synthetic data, combining computational simplicity with physics-informed strategies. Focusing on the diluted Ising model, which lacks an…
One of the most notable features in repulsive particle based active matter systems is motility-induced-phase separation (MIPS) where a dense, often crystalline phase coexists with a low density fluid. In most active matter studies, the…
Adding a small amount of passive (Brownian) particles to a two-dimensional dense suspension of repulsive active Brownian particles does not affect the appearance of a motility-induced phase separation into a dense and a dilute phase, caused…
In the present work, with the intent of exploring the out-of-equilibrium polymerization of active patchy particles in linear chains, we study a suspension of active bivalent Brownian particles (ABBPs). At all studied temperatures and…
The detection of phase transitions is a fundamental challenge in condensed matter physics, traditionally addressed through analytical methods and direct numerical simulations. In recent years, machine learning techniques have emerged as…
We demonstrate how to explore phase diagrams with automated and unsupervised machine learning to find regions of interest for possible new phases. In contrast to supervised learning, where data is classified using predetermined labels, we…
Motility-induced wall aggregation of active Brownian particles (ABPs) is a well-studied phenomenon. Here, we study the aggregation of ABPs on porous walls, which allows the particles to penetrate through at large motility. We show that the…
Self-propelled particles include both self-phoretic synthetic colloids and various micro-organisms. By continually consuming energy, they bypass the laws of equilibrium thermodynamics. These laws enforce the Boltzmann distribution in…
We study motility-induced phase separation~(MIPS) in active AB binary mixtures undergoing the chemical reaction $A \rightleftharpoons B$. Starting from the evolution equations for the density fields $\rho_i(\vec r, t)$ describing MIPS, we…
Active colloidal particles create flow around them due to non-equilibrium process on their surfaces. In this paper, we infer the activity of such colloidal particles from the flow field created by them via deep learning. We first explain…
Motility-induced phase separation (MIPS) is a central collective phenomenon in active matter, theoretically established in the overdamped regime. We discover that the dynamical origin of MIPS is fundamentally altered by inertia, which…
Controlled activity of active entities interacting with a passive environment can generate emergent system-level phenomena, positioning such systems as promising platforms for potential downstream applications in targeted drug delivery,…
We study the phase behavior of polar Active Brownian Particles moving in two-spatial dimensions and interacting through volume exclusion and velocity alignment. We combine particle-based simulations of the microscopic model with a simple…
Active particle systems are a class of non-equilibrium systems composed of self-propelled Brownian particles; through interactions between particles within the system, a variety of intriguing collective behaviors can emerge. Based on…
Motility-induced phase separation (MIPS) is a nonequilibrium phase separation that has a different origin from equilibrium phase separation induced by attractive interactions. Similarities and differences in collective behaviors between…