WAVELET SCATTERING REGRESSION OF QUANTUM CHEMICAL ENERGIES

被引:75
作者
Hirn, Matthew [1 ,2 ]
Mallat, Stephane [3 ]
Poilvert, Nicolas [4 ,5 ]
机构
[1] Michigan State Univ, Dept Computat Math Sci & Engn, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Math, E Lansing, MI 48824 USA
[3] Ecole Normale Super, Dept Informat, 45 Rue Ulm, F-75005 Paris, France
[4] Penn State Univ, Millennium Sci Complex, University Pk, PA 16801 USA
[5] Bay Labs Inc, San Francisco, CA USA
基金
美国国家科学基金会;
关键词
wavelet; scattering; multiscale; nonlinear regression; invariant dictionary; molecular energy; density functional theory; convolutional network; DENSITY; POTENTIALS; ALGORITHM; MOLECULES; CHEMISTRY;
D O I
10.1137/16M1075454
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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 multi scale 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.
引用
收藏
页码:827 / 863
页数:37
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