Ordering through learning in two-dimensional Ising spins.

We study two-dimensional Ising spins, evolving through reinforcement learning using their state, action, and reward. The state of a spin is defined by whether it is in the majority or minority with its nearest neighbors. The spin updates its state using an ε-greedy algorithm.

Polar swimmers induce several phases in active nematics.

Swimming bacteria in passive nematics in the form of lyotropic liquid crystals are defined as a new class of active matter known as living liquid crystals in recent studies. It has also been shown that liquid crystal solutions are promising candidates for trapping and detecting bacteria. We ask the question, can a similar class of matter be designed for background nematics which are also active? Hence, we developed a minimal model for the mixture of polar particles in active nematics.