V. Korolev

发表

Accelerated modeling of interfacial phases in the Ni-Bi system with machine learning interatomic potential

V. Korolev, A. Mitrofanov, Y. Kucherinenko, 2020 .

Neural Network Based Modeling of Grain Boundary Complexions Localized in Simple Symmetric Tilt Boundaries Σ3 (111) and Σ5 (210)

V. Korolev, A. Mitrofanov, Yu. M. Nevolin, 2020, Colloid Journal.

Machine-learning-assisted search for functional materials over extended chemical space

V. Korolev, A. Mitrofanov, A. Eliseev, 2020, Materials Horizons.

Aroma molecules as dynamic volatile surfactants: functionality beyond the scent.

J. Gutmann, V. Korolev, P. Protsenko, 2019, ACS applied materials & interfaces.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

V. Korolev, A. Mitrofanov, Valery Tkachenko, 2020 .

Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning

V. Korolev, A. Mitrofanov, E. Goodilin, 2020, 2006.14302.

A universal similarity based approach for predictive uncertainty quantification in materials science

V. Korolev, Iurii Nevolin, Pavel Protsenko, 2022, Scientific reports.

Aroma molecules as dynamic volatile surfactants: functionality beyond the scent.

J. Gutmann, V. Korolev, P. Protsenko, 2019, ACS applied materials & interfaces.

Accelerated Modeling of Interfacial Phases in the Ni-Bi System with Machine Learning Interatomic Potential

V. Korolev, A. Mitrofanov, Y. Kucherinenko, 2020, Scripta Materialia.

Transferable and extensible machine learning derived atomic charges for modeling metal-organic frameworks

V. Korolev, A. Mitrofanov, Valery Tkachenko, 2019, 1905.12098.

Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules

Valery Tkachenko, Vadim Korolev, Kirill Karpov, 2021, The journal of physical chemistry letters.

Graph Convolutional Neural Networks as "General-Purpose" Property Predictors: The Universality and Limits of Applicability

Alexandru Korotcov, Valery Tkachenko, Vadim Korolev, 2019, J. Chem. Inf. Model..

Molecular Dynamics Simulation of Unstable Equilibrium of a Spherical Nucleus for Determining the Interfacial Energy in a Pb–Cu Two-Component System

V. Korolev, P. Protsenko, V. Samsonov, 2019, Colloid Journal.

A Simple Automatized Tool for Exchange-Correlation Functional Fitting.

V. Korolev, A. Mitrofanov, V. Petrov, 2020, The journal of physical chemistry. A.

Accurate, interpretable predictions of materials properties within transformer language models

V. Korolev, Pavel Protsenko, 2023, Patterns.

Layer Shift Factor in Layered Hybrid Perovskites: Univocal Quantitative Descriptor of Composition–Structure–Property Relationships

V. Korolev, A. Mitrofanov, E. Goodilin, 2020, 2010.02309.

Parametrization of Nonbonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach

Vadim Korolev, Thomas A. Manz, Yuriy M. Nevolin, 2021, J. Chem. Inf. Model..

Comparing Five and Lower-Dimensional Grain Boundary Character and Energy Distributions in Copper: Experiment and Molecular Statics Simulation

K. McKenna, V. Korolev, Y. Kucherinenko, 2022, Metallurgical and Materials Transactions A.

Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites

V. Korolev, A. Mitrofanov, E. Goodilin, 2021, Chemistry of Materials.

Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials

V. Korolev, A. Mitrofanov, Valery Tkachenko, 2020, Chemistry of Materials.

Contribution of Torque Terms to Capillary Equilibrium Along Triple Junctions in Polycrystalline Copper

V. Korolev, Y. Kucherinenko, P. Protsenko, 2018, Metallurgical and Materials Transactions A.

Statistics of GB misorientations in 2D polycrystalline copper foil

V. Korolev, Y. Kucherinenko, P. Protsenko, 2017 .

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design

V. Korolev, Artem Mitrofanov, 2023, J. Chem. Inf. Model..