A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles
Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray scattering (SAXS) is an important characterization technique for LNPs, but recovering internal structure and size distribution from SAXS is an inverse problem with non-unique solutions. Realistic models are often too expensive for systematic exploration. We introduce a machine-learning-accelerated, differentiable framework for SAXS analysis of heterogeneous, polydisperse LNPs. The forward model combines a core-shell particle with a Gaussian random-field interior, a neural surrogate for the monodisperse SAXS map,...
| Authors | Maria Bånkestad, Sandra Barman, Magnus Röding et al. |
|---|---|
| Primary category | physics.comp-ph |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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