Quantum Energy Regression using Scattering Transforms
This addresses limitations in quantum energy regression for computational chemistry, though it appears incremental as it builds on existing scattering transform methods.
The authors tackled the problem of quantum mechanical energy regression by introducing a scattering transform of an intermediate electron density representation, achieving state-of-the-art accuracy for planar molecules.
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 possesses 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.