2024/12/20 by Chi-Huan Tung, Lijie Ding, Tung, Chi-Huan +18 · 3 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Computer Graphics and Visualization Techniques #Elasticity and Wave Propagation #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Soft Condensed Matter (cond-mat.soft)
paper · pdf · doi:10.48550/arxiv.2412.15474
openalex publication_date 2024/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions -- two representative soft matter systems -- we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.