A Retrieval-Augmented LLM Framework for Automated Small-Angle Scattering Data Analysis

A retrieval-augmented language model framework is presented for generating real-space structures and simulating their small-angle scattering (SAS) profiles in Python. In this workflow, the language model translates user requests into executable code that constructs the desired geometry, which is then passed to the validated Monte Carlo Distribution Function Method (MC-DFM), a numerical solution to the Debye scattering equation that computes the scattering intensity. Domain-specific instructions and examples are supplied through retrieval to improve reliability and consistency. The approach is demonstrated on a range of benchmark and user-defined systems, including spheres, cylinders, core-shell particles, spherical clusters, anisotropic cube assemblies, superlattices, protein-based cargo-cage structures, and tube-like protein assemblies. The framework captures expected scattering features for monodisperse, polydisperse, and mixed assemblies, including contrast variation and invariant-based weighting for complex superlattice populations. These results show that retrieval-augmented language models can serve as practical interfaces for rapid and reproducible small-angle scattering model generation and data analysis.