Quantum Energy Regression using Scattering Transforms
Machine Learning
2016-05-23 v3 Computer Vision and Pattern Recognition
Chemical Physics
Computational Physics
Quantum Physics
Abstract
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.
Cite
@article{arxiv.1502.02077,
title = {Quantum Energy Regression using Scattering Transforms},
author = {Matthew Hirn and Nicolas Poilvert and Stéphane Mallat},
journal= {arXiv preprint arXiv:1502.02077},
year = {2016}
}
Comments
9 pages, 2 figures, 1 table. v2: Correction to Section 4.3. v3: Replaced by arXiv:1605.04654