Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation...

Area of Science:

• Physical Chemistry
• Chemical Physics
• Computational Chemistry

Background:

• The formic acid-ammonia dimer is a key model system for studying hydrogen-bonded complexes.
• Double proton transfer is a significant phenomenon in such systems, influencing their properties.
• Previous studies reported microwave spectra and initial barrier calculations.

Purpose of the Study:

• To develop a full-dimensional potential energy surface (PES) for the formic acid-ammonia dimer.
• To achieve quantitative accuracy, specifically the Coupled Cluster Singles Doubles with Perturbative Triples (CCSD(T)) level.
• To investigate the double proton transfer mechanism and associated properties.

Main Methods:

• Utilized two closely related Δ-machine learning methods for PES construction.
• Ensured smooth and accurate dissociation of the PES.
• Employed a 2D quantum model to estimate tunneling splitting.

Main Results:

• A highly accurate, full-dimensional PES for the formic acid-ammonia dimer was generated.
• The ground vibrational-state tunneling splitting was calculated to be less than 10^-4 cm^-1.
• Dipole moment calculations along the reaction coordinate revealed mildly ionic character at the minimum and strongly ionic character at the double-well barrier.

Conclusions:

• The developed PES accurately describes the formic acid-ammonia dimer's potential energy landscape.
• The calculations provide crucial insights into the double proton transfer dynamics and the system's electronic structure.
• This work advances the understanding of hydrogen bonding and proton transfer in molecular complexes.