Neural Network Model for Structure Factor of Polymer Systems

TL;DR

This paper introduces a neural network model that efficiently predicts the structure factor of polymer chains, simplifying calculations across different conditions and enabling estimation of polymer properties from experimental data.

Contribution

A deep neural network model that accurately and efficiently predicts the structure factor of polymer chains without region-specific calculations.

Findings

Neural network achieves comparable accuracy to traditional methods.

Model predicts contour and Kuhn lengths from scattering data.

Significantly reduces computational resources needed.

Abstract

As an important physical quantity to understand the internal structure of polymer chains, the structure factor is being studied both in theory and experiment. Theoretically, the structure factor of Gaussian chains have been solved analytically, but for wormlike chains, numerical approaches are often used, such as Monte Carlo (MC) simulations, solving modified diffusion equation (MDE), etc. In those works, the structure factor needs to be calculated differently for different regions of the wave vector and chain rigidity, and some calculation processes are resource consuming. In this work, by training a deep neural network (NN), we obtained an efficient model to calculate the structure factor of polymer chains, without considering different regions of wavenumber and chain rigidity. Furthermore, based on the trained neural network model, we predicted the contour and Kuhn length of some polymer chains by using scattering experimental data, and we found our model can get pretty reasonable predictions. This work provides a method to obtain structure factor for polymer chains, which is as good as previous, and with a more computationally efficient. Also, it provides a potential way for the experimental researchers to measure the contour and Kuhn length of polymer chains.