Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

dc.contributor.authorReddy Akepati, Sri Vishnuvardhan
dc.contributor.authorGupta, Nitant
dc.contributor.authorShah, Jay
dc.contributor.authorKronenberger, Stephen
dc.contributor.authorVenkat, Vaibhav
dc.contributor.authorSridhar, Rohan Adhikari
dc.contributor.authorBianco, Simona
dc.contributor.authorAdams, Dave J.
dc.contributor.authorJayaraman, Arthi
dc.date.accessioned2025-12-03T22:43:04Z
dc.date.available2025-12-03T22:43:04Z
dc.date.issued2025-11-26
dc.descriptionThis article was originally published in ACS Measurement Science Au. The version of record is available at: https://doi.org/10.1021/acsmeasuresciau.5c00141 This publication is licensed under CC-BY-NC-ND 4.0 .https://creativecommons.org/licenses/by-nc-nd/4.0/ © 2025 The Authors. Published by American Chemical Society
dc.description.abstractWe present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged one-dimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D-) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2D-SAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.
dc.description.sponsorshipThis study was funded by the Department of Energy BES SC0023264 (A.J., S.A., J.S.) and Leverhulme Trust (RPG-2022-324) (D.A.). DARWIN supercomputing system was used for the computing done in this work: DARWIN─A Resource for Computational and Data-Intensive Research at the University of Delaware and in the Delaware Region, which is supported by the NSF under grant no. 1919839, Rudolf Eigenmann, Benjamin E. Bagozzi, Arthi Jayaraman, William Totten, and Cathy H. Wu, University of Delaware, 2021. University of Glasgow provided financial support for S.B. during her experiments.
dc.identifier.citationAkepati, S. V. R., Gupta, N., Shah, J., Kronenberger, S., Venkat, V., Adhikari Sridhar, R., Bianco, S., Adams, D. J., & Jayaraman, A. (2025). Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials. ACS Measurement Science Au. https://doi.org/10.1021/acsmeasuresciau.5c00141
dc.identifier.issn2694-250X
dc.identifier.urihttps://udspace.udel.edu/handle/19716/36776
dc.language.isoen_US
dc.publisherACS Measurement Science Au
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSAXS
dc.subjectSANS
dc.subjectMachine Learning
dc.subjectCREASE-2D
dc.subjectGenetic Algorithms
dc.subjectDipeptide Solutions
dc.subjectCREASE
dc.subjectScattering Analysis
dc.subjectStructure Generation
dc.titleTutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials
dc.typeArticle

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