Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem”

Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem”

Kirkpatrick et al . (Reports, 9 December 2021, p. 1385) trained a neural network–based DFT functional, DM21, on fractional-charge (FC) and fractional-spin (FS) systems, and they claim that it has outstanding accuracy for chemical systems exhibiting strong correlation. Here, we show that the ability...

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Veröffentlicht in:Science (American Association for the Advancement of Science) 2022-08, Vol.377 (6606), p.eabq3385-eabq3385
Hauptverfasser: Gerasimov, Igor S., Losev, Timofey V., Epifanov, Evgeny Yu, Rudenko, Irina, Bushmarinov, Ivan S., Ryabov, Alexander A., Zhilyaev, Petr A., Medvedev, Michael G.
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Sprache:eng
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Zusammenfassung:Kirkpatrick et al . (Reports, 9 December 2021, p. 1385) trained a neural network–based DFT functional, DM21, on fractional-charge (FC) and fractional-spin (FS) systems, and they claim that it has outstanding accuracy for chemical systems exhibiting strong correlation. Here, we show that the ability of DM21 to generalize the behavior of such systems does not follow from the published results and requires revisiting.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.abq3385