Connecting structure and dynamics in a model of confluent cell tissues using machine learning

Cellular motion in dense tissues often consists of neighbor-swapping events or rearrangements. The rearrangements underlie the glassy dynamics and much of the collective dynamics of dense disordered cellular packings. Here we present a machine learning (ML) approach that links the local disordered structure surrounding a cell with the propensity of the cell to rearrange in a Voronoi cell vertex model. ``Softness,'' S, an ML-derived quantity originally introduced to quantify the link between local structure and rearrangements in inert glassy liquids, provides an effective proxy for a cell's probability to rearrange, P_R. The local structural features that determine the softness of a cell are quantified. Decreasing temperature lowers P_R for a given value of S, but the distribution of S also shifts up, opposing the change, leading to previously-observed sub-Arrhenius dynamics. This contrasts with the behavior of Lennard-Jones glassy liquids, where the distribution of S shifts down with decreasing temperature, leading to super-Arrhenius dynamics.