UV Photoionization of Sodium-Doped Formic Acid Clusters

Photoionization, Structures and Energetics of Na-doped Formic Acid-Water Clusters

The influence of formic acid on water cluster aggregation has been investigated experimentally by mass spectrometry and tunable UV laser ionization applied to Na-doped clusters formed in the supersonic expansion of water vapors seeded with formic acid (FA) as well as theoretically using high level quantum chemistry methods. The mass spectra of Na-FA(H 2 O) n clusters show an enlarging of mass distribution toward heavier clusters with respect the Na-(H 2 O) n clusters, suggesting similar mass distribution in neutral clusters and an influence of formic acid in water aggregation. DFT and coupled-cluster type (DLPNO-CCSD(T)) calculations have been used to calculate structures and energetics of neutral and ionized Na-FA(H 2 O) n as well as neutral FA(H 2 O) n . Na-doped clusters are characterized by very stable geometries. The theoretical adiabatic ionization potential values match pretty well the measured appearance energies and the calculated first six electronic excited states show Rydberg-type characters, indicating possible autoionization contributions in the mass spectra. Finally, theoretical calculation on neutral FA(H 2 O) n clusters show the possibility of a similarly stable structures in small clusters containing up to n=4-5 water molecules, where FA interacts significantly with waters.This suggests that FA can compete with water molecules in the starting stage of the aggregation process, by forming stable nucleation seed.

Modeling microsolvation clusters with electronic-structure calculations guided by analytical potentials and predictive machine learning techniques

We propose a new methodology to study, at the density functional theory (DFT) level, the clusters resulting from the microsolvation of alkali-metal ions with rare-gas atoms. The workflow begins with a global optimization search to generate a pool of low-energy minimum structures for different cluster sizes. This is achieved by employing an analytical potential energy surface (PES) and an evolutionary algorithm (EA). The next main stage of the methodology is devoted to establish an adequate DFT approach to treat the microsolvation system, through a systematic benchmark study involving several combinations of functionals and basis sets, in order to characterize the global minimum structures of the smaller clusters. In the next stage, we apply machine learning (ML) classification algorithms to predict how the low-energy minima of the analytical PES map to the DFT ones. An early and accurate detection of likely DFT local minima is extremely important to guide the choice of the most promising low-energy minima of large clusters to be re-optimized at the DFT level of theory. In this work, the methodology was applied to the Li+Krn (n = 2-14 and 16) microsolvation clusters for which the most competitive DFT approach was found to be the B3LYP-D3/aug-pcseg-1. Additionally, the ML classifier was able to accurately predict most of the solutions to be re-optimized at the DFT level of theory, thereby greatly enhancing the efficiency of the process and allowing its applicability to larger clusters.