  Morse projections are well-known in chemistry and allow one, within a Morse
potential approximation, to redefine the potential in a simple quadratic form.
The latter, being a non-linear transform, is also very helpful for machine
learning methods as they improve the performance of models by projecting the
feature space onto more well-suited coordinates. Usually, the Morse projection
parameters are taken from numerical benchmarks. We investigate the effect of
changing these parameters latter on the model learning, as well as using the
machine learning method itself to make the parameters decision. We find that
learning is not necessarily improved by the latter and that general Morse
projections are extremely susceptible to changes in the training data.
