Formic Acid-Ammonia Heterodimer: A New Δ-Machine Learning CCSD(T)-Level Potential Energy Surface Allows Investigation of the Double Proton Transfer

Paul L. Houston, Chen Qu, Qi Yu, Priyanka Pandey, Riccardo Conte, Apurba Nandi, Joel M. Bowman, Stephen G. Kukolich

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The formic acid-ammonia dimer is an important example of a hydrogen-bonded complex in which a double proton transfer can occur. Its microwave spectrum has recently been reported and rotational constants and quadrupole coupling constants were determined. Calculated estimates of the double-well barrier and the internal barriers to rotation were also reported. Here, we report a full-dimensional potential energy surface (PES) for this complex, using two closely related Δ-machine learning methods to bring it to the CCSD(T) level of accuracy. The PES dissociates smoothly and accurately. Using a 2d quantum model the ground vibrational-state tunneling splitting is estimated to be less than 10-4 cm-1. The dipole moment along the intrinsic reaction coordinate is calculated along with a Mullikan charge analysis and supports the mildly ionic character of the minimum and strongly ionic character at the double-well barrier.

Original languageEnglish (US)
Pages (from-to)1821-1828
Number of pages8
JournalJournal of Chemical Theory and Computation
Volume20
Issue number5
DOIs
StatePublished - Mar 12 2024
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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