Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem”
Igor S. Gerasimov, Timofey V. Losev, Evgeny Yu. Epifanov, Irina Rudenko, Иван С. Бушмаринов, Alexander Ryabov, Petr Zhilyaev, Michael G. Medvedev
Abstract
. (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.
Topics & Concepts
ElectronStatistical physicsDensity functional theoryCharge (physics)Artificial neural networkSpin (aerodynamics)PhysicsComputer scienceTheoretical physicsApplied mathematicsMathematicsArtificial intelligenceQuantum mechanicsThermodynamicsMachine Learning in Materials ScienceComputational Drug Discovery MethodsAdvanced Chemical Physics Studies