Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning.

Experimental and theoretical studies on the compositional changes of new particle formation in the nucleation and initial growth stages of acid-base systems (2 and 5 nm) are extremely challenging. This study proposes a machine learning method for predicting the composition change of the sulfuric acid-dimethylamine system in the transformation from monomer to nanoparticle by learning the structure and composition information on small-sized sulfuric acid (SA)-dimethylamine (DMA) molecular clusters. Based on this method and changes in components, we found that the sulfuric acid-dimethylamine growth was mainly through the alternate adsorption of (SA)(DMA), (SA)(DMA), and (SA) clusters at the early stage of nucleation, which accounted for about 70, 20, and 10%, respectively. This can explain the nature of possible changes in cluster acidity during the initial nucleation stage for the sulfuric acid-dimethylamine system. This method can also predict the base-stabilization mechanism of the sulfuric acid-dimethylamine system without relying on any experimental data, thereby yielding results that are consistent with those of previous experimental measurement.