•  Python library written in C++ meant to provide an algorithmic base to integrate vertex models and add/remove/combine custom forces and integrators.

•  Thesis.
•  Thesis on HAL.
•  Code developed for the project (mainly C/C++ [simulation] and  Python [analysis]).

•  Code developed for the project (mainly C/C++ [simulation] and  Python [analysis]).
•  Wiki of the project with details on our models and methods, theoretical developments, various plots and movies.

We have worked on a numerical model system of polydisperse active Brownian disks with purely repulsive interparticle harmonic potential, inspired by Fily et al. [Soft Matter 10, 2132-2140 (2014)]. Despite its simplicity, this model displays interesting collective behaviours: motility-induced phase separation (MIPS) [Cates and Tailleur, Annu. Rev. Condens. Matter Phys. 6, 219-244 (2015)] and long-ranged displacement correlations, even on time scales greater than the rotational diffusion time. We showed that the transition from the fluid to the phase separated regime, which characterises MIPS, is accompanied by increased dynamical heterogeneity. Moreover, we provided evidence of an \(r^{-2}\) decay of shear strain correlations on time scales short compared to the rotational diffusion time, both in the dense region of our phase separated system at high activity and in the truly glassy phase [Keta and Rottler, EPL 125, 58004 (2019)]. We attributed the origin of the \(r^{-2}\) decay to elastic interactions that lead to similar behaviour in passive glass formers.

•  Code developed for the project (mainly  Python).
•  Wiki of the project with details on our model and methods as well as various plots.

We adapted models and methods which were initially used to study shear jamming of frictionless soft 2D disks in order to consider 3D ellipsoids, and in particular spheroids — ellispoids of revolution.

•  Code developed for the project (mainly C).
•  Wiki of the project with a summary of some recent findings on shear jamming and details on our model and methods.

We aimed at using data obtained from drop impacts on single micrometric defects to predict the phenomenology of impacts on more complex networks.