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We demonstrate that deep learning techniques can be used to predict motilityinduced phase separation (MIPS) in suspensions of active Brownian particles(ABPs) by creating a notion of phase at the particle level. Using a fullyconnected network in conjunction with a graph neural network we use individualparticle features to predict to which phase a particle belongs. From this, weare able to compute the fraction of dilute particles to determine if the systemis in the homogeneous dilute, dense, or coexistence region. Our predictions arecompared against the MIPS binodal computed from simulation. The strongagreement between the two suggests that machine learning provides an effectiveway to determine the phase behavior of ABPs and could prove useful fordetermining more complex phase diagrams.

Machine Learning for Phase Behavior in Active Matter Systems