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Neural network interaction potentials for para-hydrogen with flexible molecules.

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We developed a machine learning method to create accurate interaction potentials for molecular impurities in para-hydrogen (pH2) clusters. This advances understanding of quantum solvation and intermolecular forces.

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Area of Science:

  • Quantum Chemistry
  • Intermolecular Forces
  • Condensed Matter Physics

Background:

  • Understanding molecular impurities in para-hydrogen (pH2) clusters is crucial for studying intra- and intermolecular interactions.
  • The coupling between pH2 and molecular impurities, impacting superfluidity, is not well understood.
  • Accurate quantum simulations require precise interaction potentials.

Purpose of the Study:

  • To develop a data-driven approach for generating accurate impurity-pH2 interaction potentials.
  • To enable detailed quantum simulations of molecular impurities in pH2 environments.
  • To investigate the impact of molecular flexibility and quantum effects on solvation.

Main Methods:

  • Utilized machine learning, specifically high-dimensional neural network potentials (NNPs).
  • Employed the adiabatic hindered rotor (AHR) averaging technique to account for pH2 nuclear spin statistics.
  • Generated AHR-averaged NNPs at coupled cluster (CCSD(T*)-F12a/aVTZcp) accuracy in an automated manner.
  • Applied the method to water (H2O) and protonated water (H3O+) impurities.

Main Results:

  • Developed highly accurate AHR-averaged NNPs for H2O-pH2 and H3O+-pH2 interactions.
  • Demonstrated reliable description of interactions for both quasi-rigid and flexible molecules.
  • Path integral simulations revealed significant impact of H3O+ inversion on pH2 microsolvation shells.

Conclusions:

  • The automated, data-driven protocol generates accurate potentials for studying pH2 quantum solvation.
  • This methodology facilitates research on a wide range of molecular impurities in bosonic quantum solvents.
  • The findings provide atomistic insights into the complex interactions within pH2 clusters.