Global Analytical Potential Energy Surfaces for High‐resolution Molecular Spectroscopy and Reaction Dynamics

Abstract

Analytical representations of potential energy hypersurfaces for the nuclear motion in polyatomic molecules from ab initio theory and experiment are discussed in a general way. The qualification of potential hypersurface representations from ab initio theory regarding the description of experimental data from rovibrational high-resolution spectroscopy and chemical reaction kinetics is analyzed in more detail for a restricted group of molecules including methane, CH 4 , ammonia, NH 3 , H 2 O 2 , and (HF) 2 . Current methods for the derivation of analytical representations of potential energy surfaces as well as some applications are reviewed.

From quantum chemistry to dissociation kinetics: what we need to know

“…In the CH 4 case, a Morsetype MEP potential fairly well reproduces the ab initio results (see Ref. [20]). Using the MEP potential only and treating the orbital motion of H around CH 3 leads to a capture rate constant (analogous to the Langevin rate constant in electron capture, see above) such as described by phase-space theory (PST) or loose activated complex theory.…”

“…Examples [13,14] Figures 5-7 illustrate experimental data and modelling results (full curves). The high-pressure limiting behaviour corresponds to a capture of H by CH 3 (for combination) on the potential energy surface of CH 4 (see Refs [15][16][17] and, for more details, see [18][19][20]). The low-pressure behaviour reflects the properties of collisional energy transfer on CH 4 -M potential energy surfaces (see Refs [21,22]).…”

Stimulated Raman signals at conical intersections: Ab initio surface hopping simulation protocol with direct propagation of the nuclear wave function

“…The calculation of the potential V (q,t) by quantum chemistry ab initio methods becomes tedious in higher dimensions. A many body expansion of the potential, 21 keeping only the two-mode couplings, can be used to reduce the computational effort.…”

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

“…As such, a variety of different approaches to generating analytical forms for PESs have been developed and applied to a diversity of molecules and molecular complexes. Hence, PES¯tting has been and remains a vigorously studied problem; see, for example, a number of recent (since 2010) reviews [1][2][3][4][5][6][7][8][9] and the references cited therein. In this paper, the focus is on tting PESs based on high-level ab initio energies in sum-of-products form, and in particular using neural networks (NN) with exponential neurons.…”