Theoretical IR (infrared)
spectroscopy is a powerful tool for assisting chemical structure identification.
Hence, we introduce a new
approach based on the universal machine learning models of AIQM series
targeting the gold-standard coupled-cluster level, going beyond the typical DFT
accuracy.
AIQM methods, particularly, newly introduced AIQM2, can provide IR spectra with accuracy close to DFT and the speed close to a semi-empirical GFN2-xTB method. To ensure high speed and interpretability, our implementation is based on the harmonic approximation with the frequenciesscaled by factors that we found empirically.
You can easily do such calculations
yourself using MLatom, which comes with tutorials and example scripts.