  Raman spectroscopy is an important tool in the study of vibrational
properties and composition of molecules, peptides and even proteins. Raman
spectra can be simulated based on the change of the electronic polarizability
with vibrations, which can nowadays be efficiently obtained via machine
learning models trained on first-principles data. However, the transferability
of the models trained on small molecules to larger structures is unclear and
direct training on large structures in prohibitively expensive. In this work,
we first train two machine learning models to predict polarizabilities of all
20 amino acids. Both models are carefully benchmarked and compared to DFT
calculations, with neural network method found to offer better transferability.
By combining machine learning models with classical force field molecular
dynamics, Raman spectra of all amino acids are also obtained and investigated,
showing good agreement with experiments. The models are further extended to
small peptides. We find that adding structures containing peptide bonds to the
training set greatly improves predictions even for peptides not included in
training sets.
