Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Tung, Chi-Huan; Ding, Lijie; Chang, Ming-Ching; Huang, Guan-Rong; Porcar, Lionel; Wang, Yangyang; Carrillo, Jan-Michael Y.; Sumpter, Bobby G.; Shinohara, Yuya; Do, Changwoo; Chen, Wei-Ren

doi:10.1063/5.0253877

Title: Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Journal Article · 19 February 2025 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/5.0253877 · OSTI ID:2538174

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
University at Albany - State University of New York, NY (United States)
National Tsing Hua University, Hsinchu (Taiwan); National Center for Theoretical Sciences, Taipei (Taiwan)
Institut Laue-Langevin (France)

Small-angle scattering 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. Here, 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2538174

Journal Information:: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 7 Vol. 162; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

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