Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning

Abstract

We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates three orders of magnitude faster. This approach offers a scalable, automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

8 pages, 8 figures

References in corpus (8)

• Machine learning and the physical sciences
• Tutorial on Variational Autoencoders
• Dynamics of active liquid interfaces
• Tutorial: Deriving the Standard Variational Autoencoder (VAE) Loss Function
• Asymmetric fluctuations and self-folding of active interfaces
• Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers
• Machine Learning-Informed Scattering Correlation Analysis of Sheared Colloids
• Elongated particles in flow: Commentary on small angle scattering investigations

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• Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering
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• Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films