Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions
Michiel G. Wessels, Arthi Jayaraman
Abstract
] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile.
Topics & Concepts
AmphiphilePolymerScatteringMaterials scienceReverse engineeringChemical engineeringPolymer scienceComputer scienceComposite materialBiological systemPolymer chemistryEngineeringOpticsCopolymerPhysicsBiologyProgramming languageAdvanced Polymer Synthesis and CharacterizationMachine Learning in Materials ScienceMicrofluidic and Capillary Electrophoresis Applications