Thermodynamic and Transport Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes from Physics-Informed and Machine-Learned Models

TL;DR

This paper presents a data-driven, physics-informed machine learning model that accurately predicts thermodynamic and transport properties of polyethylene and higher n-alkane mixtures, surpassing traditional correlation methods.

Contribution

It introduces a novel model combining physics-informed and machine learning approaches for predicting properties of polymer mixtures, based on molecular dynamics data.

Findings

Accurately predicts properties across various conditions.

Outperforms existing correlation-based tools.

Applicable to a range of polymer and alkane mixtures.

Abstract

The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights.