activity
20192024
most citedDatabase of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning

179 citations · 539 across the 12 of their papers we have counts for

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physics.comp-ph2019★ 97 cited

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★ 61 cited

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…