{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/computational-reverse-engineering-analysis-of","title":"Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)","arxiv_id":"2401.12381","date":"2024-01-22","proceeding":null,"authors":["Sri Vishnuvardhan Reddy Akepati","Nitant Gupta","Arthi Jayaraman"],"abstract":"Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales and typically outputs scattered intensity I(q) as a function of the scattered wavevector represented by its magnitude q and azimuthal angle {\\theta}. While isotropic structures can be interpreted from azimuthally averaged 1D SAS profile, to understand anisotropic spatial arrangements, one has to interpret the 2D SAS profile, I(q,{\\theta}). In this paper, we present a new method called CREASE-2D that interprets I(q,{\\theta}) as is and outputs the relevant structural features. CREASE-2D is an extension of the 'computational reverse engineering analysis for scatting experiments' (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and a surrogate machine learning (ML) model for fast calculation of 1D 'computed' scattering profiles that are then compared to the experimental 1D scattering profiles during optimization. In CREASE-2D, which goes beyond CREASE in interpretting 2D scattering profiles, we use XGBoost as the surrogate ML model to relate structural features to the I(q,{\\theta}) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,{\\theta}) profiles match the input experimental I(q,{\\theta}). We test the performance of CREASE-2D by using as input a variety of in silico 2D SAS profiles with known structural features and demonstrate that CREASE-2D converges towards their correct structural features. We expect this method will be valuable for materials' researchers who need direct interpretation of 2D scattering profiles to explore structural anisotropy.","url_abs":"https://arxiv.org/abs/2401.12381v1","url_pdf":"https://arxiv.org/pdf/2401.12381v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"computational-reverse-engineering-analysis-of","repo_url":"https://github.com/arthijayaraman-lab/CREASE-2D","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"computational-reverse-engineering-analysis-of","repo_url":"https://zenodo.org/record/10534943","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}