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

Abstract

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.

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