A ground state potential energy surface for HONO based on a neural network with exponential fitting functions

Area of Science:

• Theoretical Chemistry
• Computational Chemistry
• Quantum Dynamics

Background:

• Accurate potential energy surfaces (PESs) are crucial for understanding molecular dynamics.
• Previous theoretical studies of nitrous acid (HONO) lacked highly accurate and validated PESs.
• Characterizing the ground state PES of HONO is essential for studying its isomers and reaction pathways.

Purpose of the Study:

• To characterize the minimum energy structures and transition states of HONO on its ground state PES.
• To develop accurate six-dimensional (6D) PESs for HONO using advanced computational methods.
• To validate the developed PESs by computing vibrational energies and comparing them with experimental and theoretical data.

Main Methods:

• High-level ab initio calculations using CCSD(T)-F12/cc-pVTZ-F12.
• Development of two 6D PESs: one using neural network fitting and another based on CBS extrapolated data.
• Validation using the multi-configuration time-dependent Hartree (MCTDH) approach for vibrational energy calculations.

Main Results:

• Characterization of key stationary points on the HONO ground state PES.
• Accurate PESs (RMSE ≈ 10 cm^-1 up to 10,000 cm^-1) were successfully developed.
• Vibrational frequencies computed on the new PESs show good agreement with experimental measurements.

Conclusions:

• The developed PESs represent the most accurate available for HONO.
• These PESs are suitable for various dynamics studies, including efficient quantum dynamics with MCTDH.
• The study provides a robust theoretical foundation for future investigations into HONO chemistry.