Irina Rudenko
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Irina Rudenko has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Alexander Ryabov
Skolkovo Institute of Science and Technology
1 shared publication - Evgeny Yu. Epifanov
N. D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, 119991 Moscow, Russian Federation
1 shared publication Igor S. Gerasimov
Department of Chemistry, Kyungpook National University, Daegu 41566, South Korea
1 shared publication- Ivan S. Bushmarinov
Perplexity AI
1 shared publication - Michael G. Medvedev
N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences Moscow Russian Federation
1 shared publication - Petr Zhilyaev
The Center for Materials Technologies, Skolkovo Institute of Science and Technology 3 , Bolshoy Boulevard 30, bld. 1, Moscow 121205, Russia
1 shared publication
Latest Publications
- 5 August 2022
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 377 (6606) , eabq3385
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 of DM21 to generalize the behavior of such systems does not follow from the published results and requires revisiting.
- 5 August 2022
- Published by American Association for the Advancement of Science (AAAS) in Science
- Vol. 377 (6606) , eabq3385
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 of DM21 to generalize the behavior of such systems does not follow from the published results and requires revisiting.