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Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol Conformers.

Apurba NandiRiccardo ConteChen QuPaul L HoustonQi YuJoel M Bowman
Published in: Journal of chemical theory and computation (2022)
Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and trans ( anti ) and gauche conformers, which are known to be very close in energy. Here we show that based on rigorous quantum calculations of the vibrational zero-point state, using a new ab initio potential energy surface (PES), the ground state resembles the trans conformer, but substantial delocalization to the gauche conformer is present. This explains experimental issues about identification and isolation of the two conformers. This "leak" effect is partially quenched when deuterating the OH group, which further demonstrates the need for a quantum mechanical approach. Diffusion Monte Carlo and full-dimensional semiclassical dynamics calculations are employed. The new PES is obtained by means of a Δ-machine learning approach starting from a pre-existing low level density functional theory surface. This surface is brought to the CCSD(T) level of theory using a relatively small number of ab initio CCSD(T) energies. Agreement between the corrected PES and direct ab initio results for standard tests is excellent. One- and two-dimensional discrete variable representation calculations focusing on the trans - gauche torsional motion are also reported, in reasonable agreement with experiment.
Keyphrases
  • density functional theory
  • molecular dynamics
  • monte carlo
  • machine learning
  • deep learning
  • molecular dynamics simulations
  • gene expression
  • human health
  • particulate matter
  • ionic liquid
  • air pollution
  • dna methylation