A predictive model for the aggregation of polycyclic aromatic compounds

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 influence the physical growth process and use machine learning to quantitatively relate these features to the propensity of these molecules to physically dimerize with other PACs. To this end, we first develop a dataset of PAC monomers along with their calculated free energies of dimerization emphasizing a set of PACs with a diverse range of properties. First, we augment existing calculations of dimerization energies with our own molecular dynamics simulations enhanced by well-tempered Metadyanmics. 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. The model is able to accurately determine the stability for both homodimerization and heterodimerization cases. Our approach also provides a data driven method to determine the molecular features most important to predicting the dimer stability. From this, we determine that the PAC properties most influential to physical dimerization are size, shape, oxygenation, and presence of rotatable bonds. 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 this approach will allow for more effective modeling of the PAC dimerization process as it facilitates the efficient prediction of dimerization propensity from easily calculable molecular features.