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Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression

The Journal of chemical physics, 2020-08, Vol.153 (7), p.074104-074104 [Peer Reviewed Journal]

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0015042 ;CODEN: JCPSA6

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  • Title:
    Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression
  • Author: Manzhos, Sergei ; Golub, Pavlo
  • Subjects: Density functional theory ; Dependence ; Dependent variables ; Energy ; Flux density ; Gaussian process ; Histograms ; Kinetic energy ; Light metals ; Mathematical analysis ; Regression
  • Is Part Of: The Journal of chemical physics, 2020-08, Vol.153 (7), p.074104-074104
  • Description: We study the dependence of kinetic energy densities (KEDs) on density-dependent variables that have been suggested in previous works on kinetic energy functionals for orbital-free density functional theory. We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KEDs for light metals and a semiconductor. We find that good quality linear regression resulting in good energy–volume dependence is possible over density-dependent variables suggested in previous literature studies. This is achieved with weighted fitting based on the KED histogram. With Gaussian process regressions, excellent KED fit quality well exceeding that of linear regressions is obtained as well as a good energy–volume dependence, which was somewhat better than that of best linear regressions. We find that while the use of the effective potential as a descriptor improves linear KED fitting, it does not improve the quality of the energy–volume dependence with linear regressions but substantially improves it with Gaussian process regression. Gaussian process regression is also able to perform well without data weighting.
  • Publisher: Melville: American Institute of Physics
  • Language: English
  • Identifier: ISSN: 0021-9606
    EISSN: 1089-7690
    DOI: 10.1063/5.0015042
    CODEN: JCPSA6
  • Source: Alma/SFX Local Collection
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