Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities
Jeffrey G. Ethier, Evan R. Antoniuk, Blair Brettmann
Abstract
the solubility of a polymer, whether it is for selecting sustainable solvents, obtaining thermodynamic parameters for phase separation, or navigating the coexistence curve. This perspective aims to discuss the different approaches of applying computational tools to predict polymer solubility, with a significant focus on machine learning techniques to capture the rapid progress in that space. We examine challenges and opportunities that remain for creating a comprehensive solubility toolset that can accelerate the design of a broad range of applications including films, membranes, and pharmaceuticals.
Topics & Concepts
Compatibility (geochemistry)Phase diagramSolubilityPerspective (graphical)PolymerSolventNanotechnologyPhase (matter)Materials scienceComputer scienceChemistryPhysical chemistryArtificial intelligenceOrganic chemistryComposite materialPhase Equilibria and ThermodynamicsMachine Learning in Materials SciencePolymer crystallization and properties