  High-throughput approximations of quantum mechanics calculations and
combinatorial experiments have been traditionally used to reduce the search
space of possible molecules, drugs and materials. However, the interplay of
structural and chemical degrees of freedom introduces enormous complexity,
which the current state-of-the-art tools are not yet designed to handle. The
availability of large molecular databases generated by quantum mechanics (QM)
computations using first principles open new venues for data science to
accelerate the discovery of new compounds. In recent years, models that combine
QM with machine learning (ML) known as QM/ML models have been successful at
delivering the accuracy of QM at the speed of ML. The goals are to develop a
framework that will accelerate the extraction of knowledge and to get insights
from quantitative process-structure-property-performance relationships hidden
in materials data via a better search of the chemical compound space, and to
infer new materials with targeted properties. In this study, we show that by
integrating well-known signal processing techniques such as discrete Fourier
transform in the QM/ML pipeline, the outcomes can be significantly improved in
some cases. We also show that the spectrogram of a molecule may represent an
interesting molecular visualization tool.
