A machine learning framework to predict the aggregation of polycyclic aromatic compounds

Abstract

The physical aggregation of polycyclic aromatic compounds (PACs) is a key step in soot inception. In this work, we set out to elucidate which molecular properties of PACs influence the physical growth process and develop a machine learning framework to quantitatively relate these features to the propensity of PACs to physically dimerize. To this end, we identify a pool of compounds with a diverse range of properties and create a dataset of PAC monomers along with their calculated free energies of dimerization, obtained via molecular dynamics simulations enhanced by well-tempered Metadynamics. We then demonstrate that a machine learning model based on the least absolute shrinkage and selection operator (Lasso) is able to quantitatively learn how molecular features contribute to physical aggregation and predict the free energy of dimerization for new pairs of molecules. Results show that our model is able to accurately determine the stability of dimers obtained from both homo- and hetero-molecular dimerization cases. Our approach provides also a data driven method to determine the molecular features most important to predicting the dimer stability. Indeed, we identified size, shape, oxygenation, and presence of rotatable bonds as the most influential characteristics of PACs that contribute to physical dimerization. This work highlights the molecular complexity of the PAC monomers that must be accounted for in order to accurately represent physical aggregation. We anticipate that our approach is key to modeling soot inception as it allows for the efficient prediction of dimerization propensity from easily calculable molecular features.