Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200318405> ?p ?o ?g. }
- W4200318405 abstract "The absence of a reliable formulation of the kinetic energy density functional has hindered the development of orbital free density functional theory. Using the data-aided learning paradigm, we propose a simple prescription to accurately model the kinetic energy density of any system. Our method relies on a dictionary of functional forms for local and nonlocal contributions, which have been proposed in the literature, and the appropriate coefficients are calculated via a linear regression framework. To model the nonlocal contributions, we explore two new nonlocal functionals-a functional that captures fluctuations in electronic density and a functional that incorporates gradient information. Since the analytical functional forms of the kernels present in these nonlocal terms are not known from theory, we propose a basis function expansion to model these seemingly difficult nonlocal quantities. This allows us to easily reconstruct kernels for any system using only a few structures. The proposed method is able to learn kinetic energy densities and total kinetic energies of molecular and periodic systems, such as H2, LiH, LiF, and a one-dimensional chain of eight hydrogens using data from Kohn-Sham density functional theory calculations for only a few structures." @default.
- W4200318405 created "2021-12-31" @default.
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- W4200318405 date "2022-01-13" @default.
- W4200318405 modified "2023-09-25" @default.
- W4200318405 title "Accurate parameterization of the kinetic energy functional" @default.
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- W4200318405 doi "https://doi.org/10.1063/5.0063629" @default.
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