Neural Network Interaction Potentials for para-Hydrogen with Flexible Molecules
arXiv:2206.08251 · doi:10.1063/5.0100953
Abstract
The study of molecular impurities in -hydrogen (H) clusters is key to push forward our understanding of intra- and intermolecular interactions including their impact on the superfluid response of this bosonic quantum solvent. This includes tagging with one or very few H, the microsolvation regime, and matrix isolation. However, the fundamental coupling between the bosonic H environment and the (ro-)vibrational motion of molecular impurities remains poorly understood. Quantum simulations can in provide the necessary atomistic insight, but very accurate descriptions of the involved interactions are required. Here, we present a data-driven approach for the generation of H interaction potentials based on machine learning techniques which retain the full flexibility of the impurity. We employ the well-established adiabatic hindered rotor (AHR) averaging technique to include the impact of the nuclear spin statistics on the symmetry-allowed rotational quantum numbers of H. Embedding this averaging procedure within the high-dimensional neural network potential (NNP) framework enables the generation of highly-accurate AHR-averaged NNPs at coupled cluster accuracy, namely CCSD(T)-F12a/aVTZcp in an automated manner. We apply this methodology to the water and protonated water molecules, as representative cases for quasi-rigid and highly-flexible molecules respectively, and obtain AHR-averaged NNPs that reliably describe the HOH and HOH interactions. Using path integral simulations we show for the hydronium cation that umbrella-like tunneling inversion has a strong impact on the first and second H microsolvation shells. The data-driven nature of our protocol opens the door to the study of bosonic H quantum solvation for a wide range of embedded impurities.
The following article has been submitted to the Journal of Chemical Physics
References in corpus (10)
- Worm Algorithm and Diagrammatic Monte Carlo: A New Approach to Continuous-Space Path Integral Monte Carlo Simulations
- Committee neural network potentials control generalization errors and enable active learning
- Superfluidity and Quantum Melting of para-Hydrogen clusters
- Structure, superfluidity, and quantum melting of hydrogen clusters
- A molecular superfluid: non-classical rotations in doped para-hydrogen clusters
- Local Superfluidity of Parahydrogen Clusters
- Microscopic molecular superfluid response: theory and simulations
- High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
- Two-dimensional molecular para-hydrogen and ortho-deuterium at zero temperature
- Three-body potential energy surface for parahydrogen