English

Wavelet Scattering Regression of Quantum Chemical Energies

Classical Analysis and ODEs 2017-11-07 v3 Chemical Physics Quantum Physics Machine Learning

Abstract

We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is represented by an electronic density function. A multiscale invariant dictionary is calculated with wavelet scattering invariants. It cascades a first wavelet transform which separates scales, with a second wavelet transform which computes interactions across scales. Sparse scattering regressions give state of the art results over two databases of organic planar molecules. On these databases, the regression error is of the order of the error produced by DFT codes, but at a fraction of the computational cost.

Keywords

Cite

@article{arxiv.1605.04654,
  title  = {Wavelet Scattering Regression of Quantum Chemical Energies},
  author = {Matthew Hirn and Stéphane Mallat and Nicolas Poilvert},
  journal= {arXiv preprint arXiv:1605.04654},
  year   = {2017}
}

Comments

Replaces arXiv:1502.02077. v2: Minor clarifications, additions, and typo corrections. v3: Minor edits. Software to reproduce the numerical results is available at: https://github.com/matthew-hirn/ScatNet-QM-2D