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This study introduces a machine learning framework to accelerate vibrational spectra prediction. The method accurately models quantum effects for water and ice, aiding new material discovery.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Spectroscopy

Background:

  • Vibrational spectroscopy is key for studying interfaces.
  • Extracting detailed information requires advanced simulations, including quantum nuclear effects.
  • Current methods are computationally expensive.

Purpose of the Study:

  • To develop a machine learning-enhanced first-principles framework for faster predictive modeling of vibrational spectra.
  • To incorporate non-Condon and quantum nuclear effects efficiently.
  • To aid the discovery of new phases of nanoconfined water.

Main Methods:

  • Developed machine learning potentials encoding quantum nuclear effects for efficient quantum trajectory generation.
  • Reformulated selection rules using derivatives of polarization and polarizabilities.
  • Employed fully-differentiable machine learning models for dielectric response tensors.

Main Results:

  • Achieved near quantitative agreement with experimental IR, Raman, and sum-frequency generation spectra for water and ice.
  • Demonstrated computational efficiency comparable to classical methods.
  • Predicted temperature-dependent vibrational spectra of monolayer water across phase transitions.

Conclusions:

  • The developed framework significantly speeds up predictive modeling of vibrational spectra.
  • The approach accurately captures quantum effects, enabling reliable predictions for interfacial phenomena.
  • This work facilitates experimental discovery of novel water phases.