As humans, we interact both visually and sterically when densely surrounded by a crowd. While our steric interactions might be reciprocal, our visual perceptions are not, as they strongly depend on the orientation of our eyes. Inspired by this, we consider a minimal model where the sole effect of visual perception is to modulate the steric potential of particles. Because self-propulsion is absent, this creates backward-moving particles, as they feel more repelled by frontal neighbors than by those behind them. We further simplify by fixing the particle orientations, initially uniformly sampled, and we ignore thermal fluctuations, meaning the particle dynamics is fully deterministic. Despite the simplicity of the model, the emerging collective phenomena at high densities are rich, ranging from crowd-defeating independent backward gliders (at high non-reciprocity) to chaotic collective motion (at intermediate non-reciprocity) that finally terminates in an absorbing phase transition (at small non-reciprocity).

The emergence of surprising collective behaviors in systems driven out of equilibrium by local energy injection at the particle level remains a central theme in the study of active matter. Recently, chaotic flows reminiscent of turbulence have garnered significant attention due to their appearance in diverse biological and physical active matter systems. In this talk, I will demonstrate how even the simplest model of active particles — self-propelled point particles — can exhibit mesoscale flows, characterized by streams and vortices, when very persistent active forces compete with crowding at high densities.In the second part, I will introduce a minimal model of non-reciprocal interactions inspired by human crowds, which generates collective flows strikingly similar to those of the self-propelled particles. Interestingly, as the system approaches the equilibrium limit by reducing non-reciprocity, it undergoes an absorbing phase transition characterized by an infinite number of absorbing states and critical exponents consistent with the conserved directed percolation universality class.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an atomistic model for glass-forming liquids. This is a notoriously difficult task, as it amounts to ergodically exploring the complex free energy landscape of a disordered and frustrated many-body system. We optimize a normalizing flow model to successfully transform high-temperature configurations of a dense liquid into low-temperature ones, near the glass transition. We perform a detailed comparative analysis with established enhanced sampling techniques developed in the physics literature to assess and rank the performance of normalizing flows against state-of-the-art algorithms. We demonstrate that machine learning methods are very promising, showing a large speedup over conventional molecular dynamics. Normalizing flows show performances comparable to parallel tempering and population annealing, while still falling far behind the swap Monte Carlo algorithm. Our study highlights the potential of generative machine learning models in scientific computing for complex systems, but also points to some of its current limitations and the need for further improvement.

Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical engineering. Under an external deformation, these materials undergo a noteworthy transition from a solid to a liquid state that reshapes the material microstructure. This yielding transition was the main theme of a workshop held from January 9 to 13, 2023 at the Lorentz Center in Leiden. The manuscript presented here offers a critical perspective on the subject, synthesizing insights from the various brainstorming sessions and informal discussions that unfolded during this week of vibrant exchange of ideas. The result of these exchanges takes the form of a series of open questions that represent outstanding experimental, numerical, and theoretical challenges to be tackled in the near future.

Equilibrium sampling of the configuration space in disordered systems requires algorithms that bypass the glassy slowing down of the physical dynamics. Irreversible Monte Carlo algorithms breaking detailed balance successfully accelerate sampling in some systems. We first implement an irreversible event-chain Monte Carlo algorithm in a model of continuously polydisperse hard disks. The effect of collective translational moves marginally affects the dynamics and results in a modest speedup that decreases with density. We then propose an irreversible algorithm performing collective particle swaps which outperforms all known Monte Carlo algorithms. We show that these collective swaps can also be used to prepare very dense jammed packings of disks.

We propose a computational strategy to quantify the temperature evolution of the timescales and length scales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, which requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time.