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Computational spectroscopy using MULTIMODE and machine-learned potentials.

Chen Qu1, Thomas C Allison2, Paul L Houston3

  • 1Independent Researcher, Toronto, Ontario M9B 0E3, Canada.

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|April 7, 2026
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Summary
This summary is machine-generated.

Accurate molecular simulations now use machine-learned potentials (MLPs). New interfaces enable advanced vibrational self-consistent field and configuration interaction (VSCF + VCI) calculations with MLPs for complex molecular spectra.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Spectroscopy

Background:

  • Advanced computational vibrational spectroscopy requires accurate molecular potentials.
  • Machine-learned potentials (MLPs) offer highly precise fits to electronic energies.
  • Integrating MLPs with post-harmonic quantum methods enables more accurate simulations.

Purpose of the Study:

  • To review methods combining MLPs with post-harmonic quantum calculations.
  • To present applications of vibrational self-consistent field and configuration interaction (VSCF + VCI) using MLPs.
  • To introduce new interfaces for the MULTIMODE code to utilize MLPs.

Main Methods:

  • Utilized vibrational self-consistent field and configuration interaction (VSCF + VCI) calculations.
  • Developed Python-based GUI and PyFort (Fortran) interfaces for MULTIMODE.
  • Employed Machine-Learned Potentials (MLPs) including PhysNet and MACE-OFF.

Main Results:

  • VSCF + VCI calculations with MLPs accurately reproduced vibrational spectra for formic acid dimer and protonated oxalate anion.
  • Demonstrated successful integration of MLPs via new MULTIMODE interfaces.
  • Validated MLPs against permutationally invariant potentials and tested MACE-OFF with H2CO.

Conclusions:

  • The developed interfaces facilitate the use of MLPs in advanced vibrational spectroscopy.
  • Combining MLPs with VSCF + VCI methods provides accurate predictions for molecular spectra.
  • This work enhances the capability of computational chemistry for studying complex molecular systems.