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We recently coined the term clusteromics as a holistic approach for obtaining insight into the chemical complexity of atmospheric molecular cluster formation and at the same time providing the foundation for thermochemical databases that can be utilized for developing machine learning models. Here, we present the first paper in the series that applies state-of-the-art computational methods to study multicomponent (SA) 0-2 (base) 0-2 clusters, with SA = sulfuric acid and base = [ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA)] with all combinations of the five bases. The initial cluster configurations are obtained using the ABCluster program and the number of relevant configurations are reduced based on PM7 and ωB97X-D/6-31++G(d,p) calculations. Thermochemical parameters are calculated based on the ωB97X-D/6-31++G(d,p) cluster structures and vibrational frequencies using the quasi-harmonic approximation. The single-point energies are refined with a high-level DLPNO-CCSD(T 0 )/aug-cc-pVTZ calculation. Using the calculated thermochemical data, we perform kinetics simulations to evaluate the potential of these small (SA) 0-2 (base) 0-2 clusters to grow into larger cluster sizes. In all cases we find that having more than one type of base molecule present in the cluster will increase the potential for forming larger clusters primarily due to the increased available vapor concentration.

Clusteromics I: Principles, Protocols, and Applications to Sulfuric Acid–Base Cluster Formation