The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event. On the other hand, the corresponding momentum distributions do not show clear event-by-event features. However, a small subset of events can be systematically differentiated even if only the momentum space information is available. In such scenarios, observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the thirdorder cumulant, the skewness, does exhibit a peak at the beam energy (E lab = 3 − 4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.