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Patterns (New York, N.Y.)|October 25, 2023
Accurate, interpretable predictions of materials properties within transformer language modelsVadim Korolev, Pavel ProtsenkoScientific Reports|September 2, 2022
A universal similarity based approach for predictive uncertainty quantification in materials scienceVadim Korolev, Iurii Nevolin, Pavel ProtsenkoIscience|April 17, 2024
The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networksVadim Korolev, Artem MitrofanovJournal of Chemical Information and Modeling|March 8, 2024
Coarse-Grained Crystal Graph Neural Networks for Reticular Materials DesignVadim Korolev, Artem MitrofanovJournal of Chemical Information and Modeling|December 23, 2025
gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property PredictionVadim Korolev, Alexey Andreevich Sorokin, Yuri KuratovJournal of Chemical Information and Modeling|December 21, 2019
Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of ApplicabilityVadim Korolev, Artem Mitrofanov, Alexandru Korotcov, et al.Scientific Reports|May 20, 2026
Enhancing composition-based materials property predictionIvan Rubtsov, Ivan Dudakov, Yuri Kuratov, et al.The Journal of Chemical Physics|October 31, 2021
A search for a DFT functional for actinide compoundsArtem Mitrofanov, Nikolai Andreadi, Vadim Korolev, et al.The Journal of Physical Chemistry Letters|September 16, 2021
Size Doesn't Matter: Predicting Physico- or Biochemical Properties Based on Dozens of MoleculesKirill Karpov, Artem Mitrofanov, Vadim Korolev, et al.The Journal of Physical Chemistry. A|March 3, 2020
Simple Automatized Tool for Exchange-Correlation Functional FittingArtem Mitrofanov, Vadim Korolev, Nikolai Andreadi, et al.Pageof 2