0538076 - ÚFCH JH 2021 RIV US eng J - Journal Article
Manzhos, S. - Golub, Pavlo
Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression.
Journal of Chemical Physics. Roč. 153, č. 7 (2020), č. článku 074104. ISSN 0021-9606. E-ISSN 1089-7690
Institutional support: RVO:61388955
Keywords : thomas-fermi approximation * functional theory * local pseudopotentials * magnesium * accurate * aluminum
OECD category: Physical chemistry
Impact factor: 3.488, year: 2020 ; AIS: 0.833, rok: 2020
Method of publishing: Open access
DOI: https://doi.org/10.1063/5.0015042

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.
Permanent Link: http://hdl.handle.net/11104/0315897