Abstract
High-quality data-driven potentials were developed aiming to predict rovibrational traits and analyze the influence of the isotopic substitution on the molecular spectroscopic properties of Ar2H+. Neural networks machine-learning approaches trained on CCSD(T)/CBS datasets were implemented. Our full-dimensional quantum MCTDH results were discussed in comparison with experimental data in gas phase and solid matrix environments, as well as against theoretical estimates available. The new data indicate that both fundamental and progression bands are dominantly driven by the strength and shape of the underlying interactions. Our simulations could enable the spectroscopic characterization of these species, assisting investigations for their astrophysical observation.
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