  Using numerical simulations of a model disk system, we demonstrate that a
machine learning generated order parameter can detect depinning transitions and
different dynamic flow phases in systems driven far from equilibrium. We
specifically consider monodisperse passive disks with short range interactions
undergoing a depinning phase transition when driven over quenched disorder. The
machine learning derived order parameter identifies the depinning transition as
well as different dynamical regimes, such as the transition from a flowing
liquid to a phase separated liquid-solid state that is not readily
distinguished with traditional measures such as velocity-force curves or
Voronoi tessellation. The order parameter also shows markedly distinct behavior
in the limit of high density where jamming effects occur. Our results should be
general to the broad class of particle-based systems that exhibit depinning
transitions and nonequilibrium phase transitions.
