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|September 23, 2024
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Automatic differentiation (AD) now computes derivatives for complex wave function methods like MP2-F12, previously lacking analytic gradients. This advancement enables new calculations, including infrared intensities, for robust quantum chemistry models.

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Theoretical Chemistry

Background:

  • Automatic differentiation (AD) enables calculation of arbitrary-order derivatives for wave function methods.
  • AD has been limited to methods with existing analytic gradients or response theory.
  • Explicitly correlated MP2 (MP2-F12) is a robust approximation lacking available derivatives.

Purpose of the Study:

  • To apply AD to the MP2-F12/3C(FIX)+CABS method, which lacks available derivatives.
  • To validate AD results by comparing them with finite-difference calculations.
  • To enable new predictions, such as IR intensities, using AD for MP2-F12.

Main Methods:

  • Implementation of AD for the MP2-F12/3C(FIX)+CABS method.
  • Comparison of AD-computed optimized geometries, dipole moments, and Hessians with finite-difference results.
  • Calculation and analysis of MP2-F12/3C(FIX)+CABS vibrational frequencies and IR intensities.

Main Results:

  • Optimized geometries from AD match finite differences to ~10^-3 Å (bond lengths) and ~10^-6° (angles).
  • Dipole moments computed via AD agree with finite differences to ~10^-6 D.
  • Hessian agreement shows a slight discrepancy (~10^-5), attributed to current AD implementation limitations.
  • Vibrational frequencies show excellent agreement within ~10^-2 cm^-1.
  • First-time prediction of MP2-F12/3C(FIX)+CABS IR intensities using AD.

Conclusions:

  • AD is successfully applied to MP2-F12/3C(FIX)+CABS, extending its utility to methods without prior analytic derivatives.
  • AD provides accurate geometries and dipole moments, with potential for improved Hessian calculations.
  • The ability to compute IR intensities opens new avenues for theoretical spectroscopy with explicitly correlated methods.