Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction
This work provides an incremental improvement for materials science researchers by enhancing energy prediction accuracy in atomic systems.
The authors tackled the problem of predicting formation energies for amorphous lithium-silicon materials by introducing a steerable wavelet scattering architecture that is equivariant to translations and rotations, achieving state-of-the-art results compared to other machine learning methods on a DFT-generated database.
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.