| Effect of wettability on sessile drop freezing: when superhydrophobicity stimulates an extreme freezing delay L Boinovich, AM Emelyanenko, VV Korolev, AS Pashinin Langmuir 30 (6), 1659-1668, 2014 | 234 | 2014 |
| Database of two-dimensional hybrid perovskite materials: open-access collection of crystal structures, band gaps, and atomic partial charges predicted by machine learning EI Marchenko, SA Fateev, AA Petrov, VV Korolev, A Mitrofanov, AV Petrov, ... Chemistry of materials 32 (17), 7383-7388, 2020 | 123 | 2020 |
| Graph convolutional neural networks as “general-purpose” property predictors: the universality and limits of applicability V Korolev, A Mitrofanov, A Korotcov, V Tkachenko Journal of chemical information and modeling 60 (1), 22-28, 2019 | 70 | 2019 |
| Transferable and extensible machine learning-derived atomic charges for modeling hybrid nanoporous materials VV Korolev, A Mitrofanov, EI Marchenko, NN Eremin, V Tkachenko, ... Chemistry of Materials 32 (18), 7822-7831, 2020 | 37 | 2020 |
| Relationships between distortions of inorganic framework and band gap of layered hybrid halide perovskites EI Marchenko, VV Korolev, SA Fateev, A Mitrofanov, NN Eremin, ... Chemistry of Materials 33 (18), 7518-7526, 2021 | 29 | 2021 |
| Layer shift factor in layered hybrid perovskites: univocal quantitative descriptor of composition–structure–property relationships EI Marchenko, VV Korolev, A Mitrofanov, SA Fateev, EA Goodilin, ... Chemistry of Materials 33 (4), 1213-1217, 2021 | 26 | 2021 |
| Machine-learning-assisted search for functional materials over extended chemical space V Korolev, A Mitrofanov, A Eliseev, V Tkachenko Materials Horizons 7 (10), 2710-2718, 2020 | 19 | 2020 |
| Preferential sorption of radionuclides on different mineral phases typical for host rocks at the site of the future Russian high level waste repository VG Petrov, IE Vlasova, AA Rodionova, VO Yapaskurt, VV Korolev, ... Applied Geochemistry 100, 90-95, 2019 | 12 | 2019 |
| A universal similarity based approach for predictive uncertainty quantification in materials science V Korolev, I Nevolin, P Protsenko Scientific Reports 12 (1), 14931, 2022 | 11 | 2022 |
| Aroma molecules as dynamic volatile surfactants: functionality beyond the scent OA Soboleva, PV Protsenko, VV Korolev, J Viktorova, A Yakushenko, ... ACS applied materials & interfaces 11 (43), 40988-40995, 2019 | 10 | 2019 |
| Accurate, interpretable predictions of materials properties within transformer language models V Korolev, P Protsenko Patterns 4 (10), 2023 | 9* | 2023 |
| A search for a DFT functional for actinide compounds A Mitrofanov, N Andreadi, V Korolev, S Kalmykov The Journal of Chemical Physics 155 (16), 2021 | 8 | 2021 |
| Accelerated modeling of interfacial phases in the Ni-Bi system with machine learning interatomic potential V Korolev, A Mitrofanov, Y Kucherinenko, Y Nevolin, V Krotov, ... Scripta Materialia 186, 14-18, 2020 | 8 | 2020 |
| Parametrization of nonbonded force field terms for metal–organic frameworks using machine learning approach VV Korolev, YM Nevolin, TA Manz, PV Protsenko Journal of Chemical Information and Modeling 61 (12), 5774-5784, 2021 | 7 | 2021 |
| Size doesn’t matter: predicting physico-or biochemical properties based on dozens of molecules K Karpov, A Mitrofanov, V Korolev, V Tkachenko The Journal of Physical Chemistry Letters 12 (38), 9213-9219, 2021 | 7 | 2021 |
| Simple automatized tool for exchange–correlation functional fitting A Mitrofanov, V Korolev, N Andreadi, V Petrov, S Kalmykov The Journal of Physical Chemistry A 124 (13), 2700-2707, 2020 | 5 | 2020 |
| Structure-related bandgap of hybrid lead halide perovskites and close-packed APbX 3 family of phases EI Marchenko, SA Fateev, VV Korolev, V Buchinskiy, NN Eremin, ... Journal of Materials Chemistry C 10 (44), 16838-16846, 2022 | 4 | 2022 |
| Statistics of GB misorientations in 2D polycrystalline copper foil VV Korolev, YV Kucherinenko, AM Makarevich, BB Straumal, ... Materials Letters 196, 377-380, 2017 | 4 | 2017 |
| Comparing Five and Lower-Dimensional Grain Boundary Character and Energy Distributions in Copper: Experiment and Molecular Statics Simulation VV Korolev, JJ Bean, YM Nevolin, YV Kucherinenko, KP McKenna, ... Metallurgical and Materials Transactions A 53 (2), 449-459, 2022 | 2 | 2022 |
| The carbon footprint of predicting CO2 storage capacity in metal–organic frameworks within neural networks V Korolev, A Mitrofanov iScience, 2024 | 1* | 2024 |
