  Accurately predicting infrared (IR) spectra in computational chemistry using
ab initio methods remains a challenge. Current approaches often rely on an
empirical approach or on tedious anharmonic calculations, mainly adapted to
semi-rigid molecules. This limitation motivates us to explore alternative
methodologies. Previous studies explored machine-learning techniques for
potential and dipolar surface generation, followed by IR spectra calculation
using classical molecular dynamics. However, these methods are computationally
expensive and require molecule-by-molecule processing. Our article introduces a
new approach to improve IR spectra prediction accuracy within a significantly
reduced computing time. We developed a machine learning (ML) model to directly
predict IR spectra from three-dimensional (3D) molecular structures. The
spectra predicted by our model significantly outperform those from density
functional theory (DFT) calculations, even after scaling. In a test set of 200
molecules, our model achieves a Spectral Information Similarity Metric of 0.92,
surpassing the value achieved by DFT scaled frequencies, which is 0.57.
Additionally, our model considers anharmonic effects, offering a fast
alternative to laborious anharmonic calculations. Moreover, our model can be
used to predict various types of spectra (Ultraviolet or Nuclear Magnetic
Resonance for example) as a function of molecular structure. All it needs is a
database of 3D structures and their associated spectra.
