Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks

作     者：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 

作者机构：Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Computer Science University at Albany - State University of New York AlbanyNY12222 United States Department of Engineering and System Science National Tsing Hua University Hsinchu30013 Taiwan Institut Laue-Langevin B.P. 156 GrenobleF-38042 Cedex 9 France Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Soft materials 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.