  The dream of machine learning in materials science is for a model to learn
the underlying physics of an atomic system, allowing it to move beyond
interpolation of the training set to the prediction of properties that were not
present in the original training data. In addition to advances in machine
learning architectures and training techniques, achieving this ambitious goal
requires a method to convert a 3D atomic system into a feature representation
that preserves rotational and translational symmetry, smoothness under small
perturbations, and invariance under re-ordering. The atomic orbital wavelet
scattering transform preserves these symmetries by construction, and has
achieved great success as a featurization method for machine learning energy
prediction. Both in small molecules and in the bulk amorphous
$\text{Li}_{\alpha}\text{Si}$ system, machine learning models using wavelet
scattering coefficients as features have demonstrated a comparable accuracy to
Density Functional Theory at a small fraction of the computational cost. In
this work, we test the generalizability of our $\text{Li}_{\alpha}\text{Si}$
energy predictor to properties that were not included in the training set, such
as elastic constants and migration barriers. We demonstrate that statistical
feature selection methods can reduce over-fitting and lead to remarkable
accuracy in these extrapolation tasks.
