Cyclic random graph models predicting giant molecules in hydrocarbon pyrolysis

TL;DR

This paper introduces a novel random graph model to predict molecular compositions in hydrocarbon pyrolysis efficiently, capturing complex molecular structures at extreme conditions with high accuracy.

Contribution

The paper presents a new random graph model with disjoint loops and assortativity correction, enabling low-cost predictions of molecular distributions in pyrolysis.

Findings

Accurately predicts size distribution of small molecules.

Successfully models largest molecule size at high pressure and temperature.

Demonstrates effectiveness across various H/C ratios.

Abstract

Hydrocarbon pyrolysis is a complex chemical reaction system at extreme temperature and pressure conditions involving large numbers of chemical reactions and chemical species. Only two kinds of atoms are involved: carbons and hydrogens. Its effective description and predictions for new settings are challenging due to the complexity of the system and the high computational cost of generating data by molecular dynamics simulations. On the other hand, the ensemble of molecules present at any moment and the carbon skeletons of these molecules can be viewed as random graphs. Therefore, an adequate random graph model can predict molecular composition at a low computational cost. We propose a random graph model featuring disjoint loops and assortativity correction and a method for learning input distributions from molecular dynamics data. The model uses works of Karrer and Newman (2010) and Newman (2002) as building blocks. We demonstrate that the proposed model accurately predicts the size distribution for small molecules as well as the size distribution of the largest molecule in reaction systems at the pressure of 40.5 GPa, temperature range of 3200K-5000K, and H/C ratio range from 2.25 as in octane through 4 as in methane.