Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials

Vibrational spectroscopy
is a cornerstone technique for
molecular
characterization and offers an ideal target for the computational
investigation of molecular materials. Building on previous comprehensive
assessments of efficient methods for infrared (IR) spectroscopy, this
study investigates the predictive accuracy and computational efficiency
of gas-phase IR spectra calculations, accessible through a combination
of modern semiempirical quantum mechanical and transferable machine
learning potentials. A composite approach for IR spectra prediction
based on the double-harmonic approximation, utilizing harmonic vibrational
frequencies in combination squared derivatives of the molecular dipole
moment, is employed. This approach allows for methodical flexibility
in the calculation of IR intensities from molecular dipoles and the
corresponding vibrational modes. Various methods are systematically
tested to suggest a suitable protocol with an emphasis on computational
efficiency. Among these methods, semiempirical extended tight-binding
(xTB) models, classical charge equilibrium models, and machine learning
potentials trained for dipole moment prediction are assessed across
a diverse data set of organic molecules. We particularly focus on
the recently reported foundational machine learning potential MACE-OFF23
to address the accuracy limitations of conventional low-cost quantum
mechanical and force-field methods. This study aims to establish a
standard for the efficient computational prediction of IR spectra,
facilitating the rapid and reliable identification of unknown compounds
and advancing automated high-throughput analytical workflows in chemistry.