Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction

Understand

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.