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

Abstract

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)₁(DMA)₁, (SA)₁(DMA)₂, and (SA)₁ 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.