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In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, I ( q ) vs q ] 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.

acs polymers au

Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions