  We demonstrate that deep learning techniques can be used to predict motility
induced phase separation (MIPS) in suspensions of active Brownian particles
(ABPs) by creating a notion of phase at the particle level. Using a fully
connected network in conjunction with a graph neural network we use individual
particle features to predict to which phase a particle belongs. From this, we
are able to compute the fraction of dilute particles to determine if the system
is in the homogeneous dilute, dense, or coexistence region. Our predictions are
compared against the MIPS binodal computed from simulation. The strong
agreement between the two suggests that machine learning provides an effective
way to determine the phase behavior of ABPs and could prove useful for
determining more complex phase diagrams.
