Quantum Energy Regression using Scattering Transforms

02/06/2015
by   Matthew Hirn, et al.
0

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.

READ FULL TEXT
research
05/16/2016

Wavelet Scattering Regression of Quantum Chemical Energies

We introduce multiscale invariant dictionaries to estimate quantum chemi...
research
01/23/2019

On basis images for the digital image representation

Digital array orthogonal transformations that can be presented as a deco...
research
01/26/2023

Graph Scattering beyond Wavelet Shackles

This work develops a flexible and mathematically sound framework for the...
research
12/28/2018

Kymatio: Scattering Transforms in Python

The wavelet scattering transform is an invariant signal representation s...
research
06/09/2014

Unsupervised Deep Haar Scattering on Graphs

The classification of high-dimensional data defined on graphs is particu...
research
01/06/2018

Multiscale Sparse Microcanonical Models

We study density estimation of stationary processes defined over an infi...
research
05/01/2018

Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties

We present a machine learning algorithm for the prediction of molecule p...

Please sign up or login with your details

Forgot password? Click here to reset