Orbital-free bond breaking via machine learning

被引：102

作者：

Snyder, John C. ^{[1
,2
]}

Rupp, Matthias ^{[3
]}

Hansen, Katja ^{[4
]}

Blooston, Leo ^{[1
]}

Mueller, Klaus-Robert ^{[5
,6
]}

Burke, Kieron ^{[1
,2
]}

机构：

[1] Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA

[2] Univ Calif Irvine, Dept Phys, Irvine, CA 92697 USA

[3] ETH, Inst Pharmaceut Sci, CH-8093 Zurich, Switzerland

[4] Max Planck Gesell, Fritz Haber Inst, D-14195 Berlin, Germany

[5] Tech Univ Berlin, Machine Learning Grp, D-10587 Berlin, Germany

[6] Korea Univ, Dept Brain & Cognit Engn, Seoul 136713, South Korea

来源：

JOURNAL OF CHEMICAL PHYSICS | 2013年 / 139卷 / 22期

基金：

美国国家科学基金会;

关键词：

KINETIC-ENERGY; DENSITY FUNCTIONALS; POTENTIALS;

D O I：

10.1063/1.4834075

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals. (C) 2013 AIP Publishing LLC.

引用

页数：10

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