Wavelet treatment of the intra-chain correlation functions of homopolymers in dilute solutions

TL;DR

This paper demonstrates that applying discrete wavelets to intra-chain correlation functions from Monte Carlo simulations improves data quality and aligns better with theoretical predictions for homopolymers in dilute solutions.

Contribution

The study introduces a wavelet-based method for parametrizing correlation functions, enhancing the accuracy of scaling exponents compared to traditional data.

Findings

Wavelet denoising improves correlation function data quality.

Better agreement with renormalisation group calculations.

Applicable to various solvent conditions and heteropolymers.

Abstract

Discrete wavelets are applied to parametrization of the intra-chain two-point correlation functions of homopolymers in dilute solutions obtained from Monte Carlo simulation. Several orthogonal and biorthogonal basis sets have been investigated for use in the truncated wavelet approximation. Quality of the approximation has been assessed by calculation of the scaling exponents obtained from des Cloizeaux ansatz for the correlation functions of homopolymers with different connectivities in a good solvent. The resulting exponents are in a better agreement with those from the recent renormalisation group calculations as compared to the data without the wavelet denoising. We also discuss how the wavelet treatment improves the quality of data for correlation functions from simulations of homopolymers at varied solvent conditions and of heteropolymers.