A. Mitrofanov

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.

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.

Mass Transport through Defects in Graphene Layers

A. Mitrofanov, A. Eliseev, A. Vasiliev, 2017 .

Tree Parzen estimator for global geometry optimization: A benchmark and database of experimental gas‐phase structures of organic molecules

A. Mitrofanov, Kirill V Karpov, D. Zankov, 2022, J. Comput. Chem..

Benzoazacrown compound: a highly effective chelator for therapeutic bismuth radioisotopes.

O. Fedorova, A. Mitrofanov, Yu. V. Fedorov, 2019, MedChemComm.

Novel Hybrid Benzoazacrown Ligand as a Chelator for Copper and Lead Cations: What Difference Does Pyridine Make

O. Fedorova, A. Mitrofanov, Yu. V. Fedorov, 2022, Molecules.

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.

Interfacial self-assembly of porphyrin-based SURMOF/graphene oxide hybrids with tunable pore size: An approach toward size-selective ambivalent heterogeneous photocatalysts

A. Mitrofanov, A. Shkolin, A. Eliseev, 2022, Applied Surface Science.

DFT Prediction of Radiolytic Stability of Conformationally Flexible Ligands

A. Mitrofanov, V. Petrov, Anastasiia Smirnova, 2022, Energies.

A search of a quantitative quantum-chemical approach for radiolytic stability prediction.

A. Mitrofanov, V. Petrov, P. Matveev, 2020, Physical chemistry chemical physics : PCCP.

Solid solutions of monazites and xenotimes of lanthanides and plutonium: Atomistic model of crystal structures, point defects and mixing properties

A. Mitrofanov, V. Petrov, E. Marchenko, 2019, Computational Materials Science.

An(III)/Ln(III) solvent extraction: Theoretical and experimental investigation of the role of ligand conformational mobility

A. Mitrofanov, V. Petrov, S. Kalmykov, 2021 .

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.

Heavy-Element Reactions Database (HERDB): Relativistic ab Initio Geometries and Energies for Actinide Compounds.

A. Mitrofanov, S. Kalmykov, P. Matveev, 2020, Inorganic chemistry.

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..

Testing a simple approach for theoretical evaluation of radiolysis products in extraction systems. A case of N,O-donor ligands for Am/Eu separation

A. Mitrofanov, Y. Ustynyuk, V. Petrov, 2017 .

A Simple Automatized Tool for Exchange-Correlation Functional Fitting.

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

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

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

Theoretical Study of Electronic Structure of Charged Fullerenes

V. Stegailov, Y. Obukhov, A. Mitrofanov, 2021, Journal of Nanomaterials.

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

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

Novel pyridine-containing azacrownethers for the chelation of therapeutic bismuth radioisotopes: Complexation study, radiolabeling, serum stability and biodistribution.

O. Fedorova, Y. Fedorov, A. Mitrofanov, 2018, Nuclear medicine and biology.

Investigating the Bismuth Complexes with Benzoazacrown Tri‐ and Tetra‐Acetates

O. Fedorova, A. Mitrofanov, S. Kalmykov, 2021, European Journal of Inorganic Chemistry.

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

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

PyRad: A software shell for simulating radiolysis with Qball package

Stepan Kalmykov, Artem Mitrofanov, Nikolai Andreadi, 2021, J. Comput. Chem..

Peculiarities of atomic hydrogen interactions with detonation nanodiamonds

G. Badun, A. Mitrofanov, A. Egorov, 2023, Mendeleev Communications.

Deep Learning Insights into Lanthanides Complexation Chemistry

A. Korotcov, A. Mitrofanov, Valery Tkachenko, 2021, Molecules.

Insights into Actinium Complexes with Tetraacetates─AcBATA versus AcDOTA: Thermodynamic, Structural, and Labeling Properties.

O. Fedorova, A. Mitrofanov, S. Kalmykov, 2023, Inorganic chemistry.