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

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 liqu...

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Veröffentlicht in:	arXiv.org 2021-10
Hauptverfasser:	Hernandes, Vinicius F, Marques, Murilo S, Bordin, José R
Format:	Artikel
Sprache:	eng
Schlagworte:	Alcohols Algorithms Computational fluid dynamics Coordination Critical point Molecular physics Neural networks Order parameters Phase transitions Physics - Computational Physics Physics - Soft Condensed Matter Polymorphism Solid phases
Online-Zugang:	Volltext
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