Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling.

ABSTRACT: Small-angle X-ray scattering (SAXS) is a characterization technique that allows for the study of colloidal interactions by fitting the structure factor of the SAXS profile with a selected model and closure relation. However, the applicability of this approach is constrained by the limited number of existing models that can be fitted analytically, as well as the narrow operating range for which the models are valid. In this work, we demonstrate a proof of concept for using an artificial neural network (ANN) trained on SAXS curves obtained from Monte Carlo (MC) simulations to predict values of the effective macroion valency (Z _eff) and the Debye length (κ^-1) for a given SAXS profile. This ANN, which was trained on 200,000 simulated SAXS curves, was able to predic

SUBMITTER: Wong K 

PROVIDER: S-EPMC11423300 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature