Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures
Vinicius F. Hernandes,
Murilo S. Marques,
José R. Bordin
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 … Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.