97 citations · 158 across the 2 of their papers we have counts for
3 papers
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.…
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…