Comment on "Pushing the frontiers of density functionals by solving the fractional electron problem".

Gerasimov, Igor S; Losev, Timofey V; Epifanov, Evgeny Yu; Rudenko, Irina; Bushmarinov, Ivan S; Ryabov, Alexander A; Zhilyaev, Petr A; Medvedev, Michael G

Gerasimov, Igor S; Losev, Timofey V; Epifanov, Evgeny Yu; Rudenko, Irina; Bushmarinov, Ivan S; Ryabov, Alexander A; Zhilyaev, Petr A; Medvedev, Michael G.

Afiliación

Gerasimov IS; Department of Chemistry, Kyungpook National University, Daegu 41566, South Korea.
Losev TV; N. D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, 119991 Moscow, Russian Federation.
Epifanov EY; Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russian Federation.
Rudenko I; N. D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, 119991 Moscow, Russian Federation.
Bushmarinov IS; National Research University Higher School of Economics, 101000 Moscow, Russian Federation.
Ryabov AA; Yandex, 119021 Moscow, Russian Federation.
Zhilyaev PA; Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region 141700, Russian Federation.
Medvedev MG; Yandex, 119021 Moscow, Russian Federation.

Science ; 377(6606): eabq3385, 2022 08 05.

Article en En | MEDLINE | ID: mdl-35926034

ABSTRACT

ABSTRACT

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.

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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Science Año: 2022 Tipo del documento: Article País de afiliación: Corea del Sur