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Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression
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SYSNO ASEP 0538076 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Data-driven kinetic energy density fitting for orbital-free DFT: Linear vs Gaussian process regression Tvůrce(i) Manzhos, S. (CA)
Golub, Pavlo (UFCH-W) ORCID, RID, SAIČíslo článku 074104 Zdroj.dok. Journal of Chemical Physics. - : AIP Publishing - ISSN 0021-9606
Roč. 153, č. 7 (2020)Poč.str. 15 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova thomas-fermi approximation ; functional theory ; local pseudopotentials ; magnesium ; accurate ; aluminum Vědní obor RIV CF - Fyzikální chemie a teoretická chemie Obor OECD Physical chemistry Způsob publikování Open access Institucionální podpora UFCH-W - RVO:61388955 UT WOS 000563905200002 EID SCOPUS 85089794538 DOI https://doi.org/10.1063/5.0015042 Anotace 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. Pracoviště Ústav fyzikální chemie J.Heyrovského Kontakt Michaela Knapová, michaela.knapova@jh-inst.cas.cz, Tel.: 266 053 196 Rok sběru 2021 Elektronická adresa http://hdl.handle.net/11104/0315897
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