179 citations · 445 across the 6 of their papers we have counts for
7 papers
A Fast and Accurate Semi-Empirical Approach for Hydrogen-Exchange Kinetic Isotope Effect Evaluation
Mikhail Rudenko, Artem Eliseev, Artem Mitrofanov +1
The kinetic isotope effect (KIE) is essential in various chemical applications from reaction mechanism studies to tritium removal from water. Traditional KIE evaluation relies on e…
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.…
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…