Atmospheric Sulfuric Acid-Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning

Jakub Kubečka*, Ivo Neefjes, Vitus Besel, Fukang Qiao, Hong-Bin Xie*, Jonas Elm

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

The formation of molecular clusters and secondary aerosols in the atmosphere has a significant impact on the climate. Studies typically focus on the new particle formation (NPF) of sulfuric acid (SA) with a single base molecule (e.g., dimethylamine or ammonia). In this work, we examine the combinations and synergy of several bases. Specifically, we used computational quantum chemistry to perform configurational sampling (CS) of (SA)0-4(base)0-4 clusters with five different types of bases: ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). Overall, we studied 316 different clusters. We used a traditional multilevel funnelling sampling approach augmented by a machine-learning (ML) step. The ML made the CS of these clusters possible by significantly enhancing the speed and quality of the search for the lowest free energy configurations. Subsequently, the cluster thermodynamics properties were evaluated at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory. The calculated binding free energies were used to evaluate the cluster stabilities for population dynamics simulations. The resultant SA-driven NPF rates and synergies of the studied bases are presented to show that DMA and EDA act as nucleators (although EDA becomes weak in large clusters), TMA acts as a catalyzer, and AM/MA is often overshadowed by strong bases.

Original languageEnglish
JournalThe journal of physical chemistry. A
Volume127
Issue9
Pages (from-to)2091-2103
Number of pages13
ISSN1089-5639
DOIs
Publication statusPublished - Mar 2023

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