Nuclear quantum effects in gas-phase 2-fluoroethanol.

Torsional motions along the FCCO and HOCC dihedrals lead to the five unique conformations of 2-fluoroethanol, of which the conformer that is gauche along both dihedrals has the lowest energy. In this work, we explore how nuclear quantum effects (NQEs) manifest in the structural parameters of the lowest energy conformer, in the intramolecular free energy landscape along the FCCO and HOCC dihedrals, and also in the infrared spectrum of the title molecule, through the use of path integral simulations. We have first developed a full dimensional potential energy surface using the reaction surface Hamiltonian framework. On this potential, we have carried out path integral molecular dynamics simulations at several temperatures starting from the minimum energy well to explore structural influences of NQEs including geometrical markers of the interaction between the OH and F groups. From the computed free energy landscapes, significant reduction of the torsional barrier is found at low temperature near the cis region of the dihedrals, which can be understood through the trends in the radii of gyration of the atomic ring polymers. We find that the inclusion of NQEs in the computation of infrared spectrum is important to obtain good agreement with the experimental band positions.

Nuclear Quantum Effects in Hydroxide Hydrate Along the H-Bond Bifurcation Pathway.

Path integral (PI) simulations are used to explore nuclear quantum effects (NQEs) in hydroxide hydrate and its perdeuterated isotopomer along the H-bond bifurcation pathway. Toward this, a new potential energy surface using the symmetric gradient domain machine learning method with ab initio data at the CCSD(T)/aug-cc-pVTZ level is built. From PI umbrella sampling (US) simulations, free energy profiles along the bifurcation coordinate are explored as a function of temperature. At ambient temperature, the bifurcation barrier is increased upon inclusion of NQEs. At low temperatures in the deep tunneling regime, the barrier is strongly decreased and flattened. These trends are examined, and the role of the O-O distance is also investigated through two-dimensional US simulations.

Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H3O2.

The singly hydrated hydroxide anion OH-(H2O) is of central importance to a detailed molecular understanding of water; therefore, there is strong motivation to develop a highly accurate potential to describe this anion. While this is a small molecule, it is necessary to have an extensive data set of energies and, if possible, forces to span several important stationary points. Here, we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reported in 2004. We describe the details of both fitting methods and then compare the two PESs with respect to precision, properties, and speed of evaluation. While the precision of the potentials is similar, the PIP PES is much faster to evaluate for energies and energies plus gradient than the sGDML one. Diffusion Monte Carlo calculations of the ground vibrational state, using both potentials, produce similar large anharmonic downshift of the zero-point energy compared to the harmonic approximation of the PIP and sGDML potentials. The computational time for these calculations using the sGDML PES is roughly 300 times greater than using the PIP one.