Machine Learning Inversion from Small-Angle Scattering for Charged Polymers

TL;DR

This paper presents a machine learning approach combined with Monte Carlo simulations to interpret small-angle scattering data for charged polymers, enabling the extraction of polymer conformational parameters with high accuracy.

Contribution

The study introduces a novel inverse mapping method using Gaussian process regression to determine polymer parameters from scattering data, incorporating detailed simulation data.

Findings

Gaussian process regression accurately predicts polymer parameters

Principal component analysis assesses structure factor features

Simulation data covers a wide range of polymer conformations

Abstract

We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.