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Characterization of phases of soft matter systems is a challenge faced inmany physicochemical problems. For polymorphic fluids it is an even greaterchallenge. Specifically, glass forming fluids, as water, can have, besidessolid polymorphism, more than one liquid and glassy phases, and even aliquid-liquid critical point. In this sense, we apply a neural network (NN)algorithm to analyze the phase behavior of a core-softened mixture ofcore-softened CSW fluids that have liquid polymorphism and liquid-liquidcritical points, similar to water. We also apply the NN to mixtures of CSWfluids and core-softened alcohols models. We combine and expand two methodsbased on bond-orientational order parameters to study mixtures, applied tomixtures of hardcore fluids by Boattini and co-authors [Molecular Physics 116,3066-3075 (2018)] and to supercooled water by Martelli and co-authors [TheJournal of Chemical Physics 153, 104503 (2020)], to include longer rangecoordination shells. With this, the trained neural network (NN) was able toproperly predict the crystalline solid phases, the fluid phases and theamorphous phase for the pure CSW and CSW-alcohols mixtures with highefficiency. More than this, information about the phase populations, obtainedfrom the NN approach, can help verify if the phase transition is continuous ordiscontinuous, and also to interpret how the metastable amorphous regionspreads along the stable high density fluid phase. These findings help tounderstand the behavior of supercooled polymorphic fluids and extend thecomprehension of how amphiphilic solutes affect the phases behavior.

Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures