  Characterization of phases of soft matter systems is a challenge faced in
many physicochemical problems. For polymorphic fluids it is an even greater
challenge. Specifically, glass forming fluids, as water, can have, besides
solid polymorphism, more than one liquid and glassy phases, and even a
liquid-liquid critical point. In this sense, we apply a neural network (NN)
algorithm to analyze the phase behavior of a core-softened mixture of
core-softened CSW fluids that have liquid polymorphism and liquid-liquid
critical points, similar to water. We also apply the NN to mixtures of CSW
fluids and core-softened alcohols models. We combine and expand two methods
based on bond-orientational order parameters to study mixtures, applied to
mixtures of hardcore fluids by Boattini and co-authors [Molecular Physics 116,
3066-3075 (2018)] and to supercooled water by Martelli and co-authors [The
Journal of Chemical Physics 153, 104503 (2020)], to include longer range
coordination shells. With this, the trained neural network (NN) was able to
properly predict the crystalline solid phases, the fluid phases and the
amorphous phase for the pure CSW and CSW-alcohols mixtures with high
efficiency. More than this, information about the phase populations, obtained
from the NN approach, can help verify if the phase transition is continuous or
discontinuous, and also to interpret how the metastable amorphous region
spreads along the stable high density fluid phase. These findings help to
understand the behavior of supercooled polymorphic fluids and extend the
comprehension of how amphiphilic solutes affect the phases behavior.
