  A general machine learning architecture is introduced that uses wavelet
scattering coefficients of an inputted three dimensional signal as features.
Solid harmonic wavelet scattering transforms of three dimensional signals were
previously introduced in a machine learning framework for the regression of
properties of small organic molecules. Here this approach is extended for
general steerable wavelets which are equivariant to translations and rotations,
resulting in a sparse model of the target function. The scattering coefficients
inherit from the wavelets invariance to translations and rotations. As an
illustration of this approach a linear regression model is learned for the
formation energy of amorphous lithium-silicon material states trained over a
database generated using plane-wave Density Functional Theory methods.
State-of-the-art results are produced as compared to other machine learning
approaches over similarly generated databases.
