Výsledky vyhledávání - "Mitrofanov, Artem P."

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Data-driven prediction of structure of metal-organic frameworks

Autor: Yakovenko, Elizaveta, Nevolin, Iurii, Chasovskikh, Anatoliy, Mitrofanov, Artem, Korolev, Vadim

Crystal structure prediction (CSP) has proven to be a highly effective route for discovering new materials. Substantial advancements have been made in CSP of inorganic and molecular crystals, while hybrid materials, including metal-organic frameworks

Externí odkaz: http://arxiv.org/abs/2408.10814

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Coarse-grained crystal graph neural networks for reticular materials design

Autor: Korolev, Vadim, Mitrofanov, Artem

Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine relative ease of synthesis and an impressive range of applications in various fields, from gas storage to biomedicine. Diverse properties arise from the

Externí odkaz: http://arxiv.org/abs/2310.19500

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

Autor: Karpov, Kirill, Mitrofanov, Artem, Korolev, Vadim, Tkachenko, Valery

The use of machine learning in chemistry has become a common practice. At the same time, despite the success of modern machine learning methods, the lack of data limits their use. Using a transfer learning methodology can help solve this problem. Thi

Externí odkaz: http://arxiv.org/abs/2107.10882

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Relationships between distortions of inorganic framework and band gap of layered hybrid halide perovskites

Autor: Marchenko, Ekaterina I., Korolev, Vadim V., Fateev, Sergey A., Mitrofanov, Artem, Eremin, Nickolay N., Goodilin, Eugene A., Tarasov, Alexey B.

The unprecedented structural flexibility and diversity of inorganic frameworks of layered hybrid halide perovskites (LHHPs) rise up a wide range of useful optoelectronic properties thus predetermining the extraordinary high interest to this family of

Externí odkaz: http://arxiv.org/abs/2105.05078

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Layer shift factor in layered hybrid perovskites -- univocal quantitative descriptor of composition-structure-property relationships

Autor: Marchenko, Ekaterina I., Korolev, Vadim V., Mitrofanov, Artem, Fateev, Sergey A., Goodilin, Eugene A., Tarasov, Alexey B.

Asceding interest of the scientific community in layered hybrid halide perovskites (LHHPs) as materials for innovative photovoltaic and optoelectronic applications led to unprecedented expansion of this family of compounds, reaching now several hundr

Externí odkaz: http://arxiv.org/abs/2010.02309

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Graph convolutional neural networks as 'general-purpose' property predictors: the universality and limits of applicability

Autor: Korolev, Vadim, Mitrofanov, Artem, Korotcov, Alexandru, Tkachenko, Valery

Nowadays the development of new functional materials/chemical compounds using machine learning (ML) techniques is a hot topic and includes several crucial steps, one of which is the choice of chemical structure representation. Classical approach of r

Externí odkaz: http://arxiv.org/abs/1906.06256

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Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials

Autor: Korolev, Vadim, Mitrofanov, Artem, Marchenko, Ekaterina, Eremin, Nickolay, Tkachenko, Valery, Kalmykov, Stepan

Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard technique to access the performance of thousands of candidates, but it

Externí odkaz: http://arxiv.org/abs/1905.12098

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