Machine Learning Representations of the Three Lowest Adiabatic Electronic Potential Energy Surfaces for the ArH 2 + Reactive System.

In this work, we present Gaussian process regression machine learning representations of the three lowest coupled 2 A' adiabatic electronic potential energy surfaces of the ArH 2 + reactive system in full dimensionality. Additionally, the nonadiabatic coupling matrix elements were calculated. These adiabatic potentials and their nonadiabatic couplings are necessary ingredients in the theoretical investigation of the nonadiabatic reaction dynamics of the Ar + H 2 + → ArH + + H and Ar + + H 2 → ArH + + H reactions, as well as the competing charge transfer process, Ar + H 2 + ↔ Ar + + H 2 . Accurate ab initio electronic structure calculations (ic-MRCI+Q/aug-cc-pVQZ), whereby the effect of spin-orbit coupling in Ar + has been accounted for through the state interaction method, serve as input for the machine learning training process. The potential energy surfaces are fitted with high accuracies, with root-mean-square errors on the order of 10 -7 eV for the three surfaces, which meet the requirements for chemical dynamics at low temperature. It was found that quite a large number of training points (of the order of 5000 ab initio points) are needed in order to achieve these accuracies due to the complex topography of these electronic surfaces.