A kernel-based machine learning potential and quantum vibrational state analysis of the cationic Ar hydride (Ar2H+)

Authors:
Open Access
Publisher website
Google Scholar
Abstract
One of the most fascinating discoveries in the last years, in the cold and low pressure regions of the Universe, was the detection of the ArH+ and HeH+ species. The identifcation of such noble gas- containing molecules in the space is the key to understand the noble gas chemistry. In the present work, we discuss the possibility of the Ar2H+ existence as a potentially detectable molecule in the interstellar medium, providing new data on possible astronomical pathways and energetics of such compound. As a first step, a data driven approach is proposed to construct a full 3D machine-learning potential energy surface via the reproducing kernel Hilbert space method. The training and testing data sets are generated from CCSD(T)/CBS[56] computations, while a validation protocol is introduced to ensure the quality of the potential. In turn, the resulting potential is employed to compute vibrational levels and molecular spectroscopic constants for the cation. In that way, the most common isotopologue in the ISM, 36Ar2H+, has been characterized for the first time, while simultaneously, comparisons with previously reported values available for the 40Ar2H+ are discussed. Our present data could serve as a benchmark for future studies on this system, as well as on higher-order cationic Ar hydrides of astrophysical interest.
Funding Information
  • European Cooperation in Science and Technology (CA1822 (MDGAS), CA21101(COS))
  • Ministerio de Ciencia e Innovación (PID2020-114654GB-I00)
  • Universidad Nacional de Colombia (Hermes code: 57565)
  • Dirección General de Universidades e Investigación (IND2018/TIC-9467)

This publication has 78 references indexed in Scilit: