Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (" P ( q ) and S ( q ) CREASE").

In this paper, we present an open-source machine learning (ML)-accelerated computational method to analyze small-angle scattering profiles [ I ( q ) vs q ] from concentrated macromolecular solutions to simultaneously obtain the form factor P ( q ) ( e.g ., dimensions of a micelle) and the structure factor S ( q ) ( e.g ., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P ( q ) from dilute macromolecular solutions (where S ( q ) ∼1) or to obtain S ( q ) from concentrated particle solutions when P ( q ) is known ( e.g ., sphere form factor). This paper's newly developed CREASE that calculates P ( q ) and S ( q ), termed as " P ( q ) and S ( q ) CREASE", is validated by taking as input I ( q ) vs q from in silico structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. We demonstrate how " P ( q ) and S ( q ) CREASE" performs if given two or three of the relevant scattering profiles- I total ( q ), I A ( q ), and I B ( q )-as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of " P ( q ) and S ( q ) CREASE" on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant-coated nanoparticles with varying extents of aggregation.