期刊
JOURNAL OF CHEMICAL PHYSICS
卷 139, 期 22, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.4834075
关键词
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资金
- NSF [CHE-1240252]
- EU PASCAL2
- DFG [MU 987/4-2, MU 987/17-1]
- Einstein Foundation
- EU [IEF 273039]
- NRF Korea [BK21]
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1240252] Funding Source: National Science Foundation
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.
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