Quantum Chemistry Energy Regression with Deep Scattering Networks
Presenter
February 25, 2015
Abstract
Stéphane Mallat
École Normale Supérieure
The authors are: Matthew Hirn, Stephane Mallat, Nicolas Poilvert
We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It has appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.