Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions

Wu, Zijie
Jayaraman, Arthi
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In this work, we present a machine learning (ML)-enhanced computational reverse-engineering analysis of scattering experiments (CREASE) approach to analyze the small-angle scattering profiles from polymer solutions with assembled semiflexible fibrils with dispersity in fibril diameters (e.g., aqueous solutions of methylcellulose fibrils). This work is an improvement over the original CREASE method [Beltran-Villegas, D. J.; J. Am. Chem. Soc., 2019, 141, 14916−14930], which identifies relevant dimensions of assembled structures in polymer solutions from their small-angle scattering profiles without relying on traditional analytical models. Here, we improve the original CREASE approach by incorporating ML for analyzing assembled semiflexible fibrillar structures with disperse fibril diameters. We first validate our CREASE approach without ML by taking as input the scattering profiles of in silico structures with known dimensions (diameter, Kuhn length) and reproducing as output those known dimensions within error. We then show how the incorporation of ML (specifically an artificial neural network, denoted as NN) within the CREASE approach improves the speed of workflow without sacrificing the accuracy of the determined fibrillar dimensions. Finally, we apply NN-enhanced CREASE to experimental small-angle X-ray scattering profiles from methylcellulose fibrils obtained by Lodge, Bates, and co-workers [Schmidt, P. W.; Macromolecules, 2018, 51, 7767−7775] to determine fibril diameter distribution and compare NN-enhanced CREASE’s output with their fibril diameter distribution fitted using analytical models. The diameter distributions of methylcellulose fibrils from NN-enhanced CREASE are similar to those obtained from analytical model fits, confirming the results by Lodge, Bates, and co-workers that methylcellulose form fibrils with consistent average diameters of ∼15–20 nm regardless of the molecular weight of methylcellulose chains. The successful implementation of NN-enhanced CREASE in handling experimental scattering profiles of complex macromolecular assembled structures with dispersity in dimensions demonstrates its potential for application toward other unconventional fibrillar systems that may not have appropriate analytical models.
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Macromolecules, copyright © 2022 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see This article will be embargoed until 12/27/2023.
chemical structure , nanofibers , physical and chemical properties , scattering , x-ray scattering
Wu, Zijie, and Arthi Jayaraman. “Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions.” Macromolecules 55, no. 24 (December 27, 2022): 11076–91.