179 citations · 539 across the 12 of their papers we have counts for
13 papers
Data-driven prediction of structure of metal-organic frameworks
Elizaveta Yakovenko, Iurii Nevolin, Anatoliy Chasovskikh +2
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 molecula…
Machine Learning Recognition of hybrid lead halide perovskites and perovskite-related structures out of X-ray diffraction patterns
E. I. Marchenko, V. V. Korolev, E. A. Kobeleva +5
Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or fals…
Artificial bees collect diverse conformers of small organic molecules
Anastasiia Smirnova, Maxim Yablonskiy, Ekaterina Marchenko +3
The conformational mobility of organic molecules defined as a variability of practically accessible conformers plays a critical role in determining electronic, chemical, and physic…
Coarse-grained crystal graph neural networks for reticular materials design
Vadim Korolev, Artem Mitrofanov
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine relative ease of synthesis and an impressive range of applications in various field…
Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
Vadim Korolev, Pavel Protsenko
Property prediction accuracy has long been a key parameter of machine learning in materials informatics. Accordingly, advanced models showing state-of-the-art performance turn into…
Structure-related bandgap of hybrid lead halide perovskites and close-packed APbX3 family of phases
Ekaterina I. Marchenko, Sergey A. Fateev, Vadim V. Korolev +4
Metal halide perovskites APbX3 (A+ = FA+ (formamidinium), MA+ (methylammonium) or Cs+, X- = I-, Br-) are considered as prominent innovative components in nowadays perovskite solar…
Size doesn't matter: predicting physico- or biochemical properties based on dozens of molecules
Kirill Karpov, Artem Mitrofanov, Vadim Korolev +1
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.…
Parametrization of Non-Bonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach
Vadim V. Korolev, Yurii M. Nevolin, Thomas A. Manz +1
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOF…
Relationships between distortions of inorganic framework and band gap of layered hybrid halide perovskites
Ekaterina I. Marchenko, Vadim V. Korolev, Sergey A. Fateev +4
The unprecedented structural flexibility and diversity of inorganic frameworks of layered hybrid halide perovskites (LHHPs) rise up a wide range of useful optoelectronic properties…
Layer shift factor in layered hybrid perovskites -- univocal quantitative descriptor of composition-structure-property relationships
Ekaterina I. Marchenko, Vadim V. Korolev, Artem Mitrofanov +3
Asceding interest of the scientific community in layered hybrid halide perovskites (LHHPs) as materials for innovative photovoltaic and optoelectronic applications led to unprecede…
Database of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning
Ekaterina I. Marchenko, Sergey A. Fateev, Andrey A. Petrov +5
We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database…
Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
Vadim Korolev, Artem Mitrofanov, Alexandru Korotcov +1
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 th…
Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials
Vadim Korolev, Artem Mitrofanov, Ekaterina Marchenko +3
Nanoporous materials have attracted significant interest as an emerging platform for adsorption-related applications. The high-throughput computational screening became a standard…