Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties

We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coeffici...

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Bibliographic Details
Published inarXiv.org
Main Authors Eickenberg, Michael, Exarchakis, Georgios, Hirn, Matthew, Mallat, Stéphane, Thiry, Louis
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.05.2018
Subjects
Algorithms
Computer Science - Computational Engineering, Finance, and Science
Computer Science - Learning
Density functional theory
Functionals
Invariants
Machine learning
Physical properties
Physics - Chemical Physics
Scattering coefficients
Statistics - Machine Learning
Wavelet analysis
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Summary:We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory. Using Gaussian-type orbital functions, we create surrogate electronic densities of the molecule from which we compute invariant "solid harmonic scattering coefficients" that account for different types of interactions at different scales. Multi-linear regressions of various physical properties of molecules are computed from these invariant coefficients. Numerical experiments show that these regressions have near state of the art performance, even with relatively few training examples. Predictions over small sets of scattering coefficients can reach a DFT precision while being interpretable.
ISSN:2331-8422
DOI:10.48550/arxiv.1805.00571