A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles

arXiv:2606.05200v1 Announce Type: cross Abstract: 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, polydispe

Source: arXiv cs.LG — read the full report at the original publisher.

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