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

Science. 2022. Vol. 377. No. 6606. Article eabq3385.

Gerasimov I., Losev T., Evgeny Yu. Epifanov, Rudenko I., Bushmarinov I., Ryabov A., Zhilyaev P., Медведев М. Г.

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.

Ключевые слова: DFT, Теория функционала плотности, глубокие нейронные сети, artificial neural network, Density functional theory (DFT)

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