3 papers
cs.LG2021
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.…
physics.comp-ph2019
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…
physics.comp-ph2019
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…