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

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(T0)/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.