The team uses the tools of statistical physics and computational modeling at different scales to study questions that range from molecular scale modeling to mechanical and thermal properties of soft disordered materials and to different aspects of statistical physics of non-equilibrium systems and collective phenomena. These studies are often based on numerical simulations (typically using molecular dynamics or Monte-Carlo techniques) of models that can be quite detailed and specific, or use various levels of coarse-graining. The statistical description of non-equilibrium systems is also investigated both by studying specific classes of simplified models, or methodological tools that can be useful to describe many different types of systems.

Researches in the PSM team are mostly conducted along the following three main axes:

1) **Mechanical and thermal properties of soft disordered materials** (J.-L. Barrat, R. Mari, K. Martens)

Plasticity of soft amorphous materials (foams, gels, cell monolayers...), coupling microscopic, mesoscopic and macroscopic approaches

Thermal and thermomechanical properties in heterogeneous elastic materials

Rheology of dense suspensions, linking microscopic interactions to macroscopic behavior

2) **Molecular scale modeling** (F. Calvo, B. Coasne, M. Joyeux)

Structure, dynamics and spectroscopy of nanoscale molecular structures (molecular clusters, nanoalloys,...)

Adsorption and transport properties of simple or complex fluids confined in nanoporous materials

Dynamics of coarse-grained DNA and DNA-protein interactions

3) **Statistical physics of non-equilibrium systems** (E. Bertin, V. Lecomte, B. Fourcade)

Interfaces in disordered media and depinning transition

Methodological tools for non-equilibrium statistical physics (large deviations, path integrals,...)

Nonequilibrium thermodynamics of steady-state driven systems

Onset of order in dry active matter (assemblies of interacting self-propelled particles)

Stochastic simulations of reaction-diffusion systems

Receptor recruitment models in the context of cell signaling.