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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

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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