Machine Learning-Informed Scattering Correlation Analysis of Sheared Colloids

TL;DR

This study combines theoretical, simulation, and machine learning methods to analyze microscopic rearrangements in sheared colloids through scattering data, enabling quantitative insights into their dynamics.

Contribution

It introduces a machine learning framework that accurately infers shear strain, non-affine rearrangements, and polydispersity from scattering correlation functions.

Findings

Gaussian process regressor retrieves parameters with low error

Machine learning inversion feasible from scattering data

Framework applicable to steady and non-steady colloidal dynamics

Abstract

We carry out theoretical analysis, Monte Carlo simulations and Machine Learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids are created and undergo a rearrangement consisting of an affine simple shear and non-affine rearrangement using Monte Carlo method. We calculate the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement, and generate a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain, and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine learning inversion from the correlation function for the polydispersity, shear strain, and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained based on the data set and can retrieve the affine shear strain, non-affine rearrangement, and polydispersity with a relative error of 3\%, 1\% and 6\%, respectively. Together, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.