  We introduce the elEmBERT model for chemical classification tasks. It is
based on deep learning techniques, such as a multilayer encoder architecture.
We demonstrate the opportunities offered by our approach on sets of organic,
inorganic and crystalline compounds. In particular, we developed and tested the
model using the Matbench and Moleculenet benchmarks, which include crystal
properties and drug design-related benchmarks. We also conduct an analysis of
vector representations of chemical compounds, shedding light on the underlying
patterns in structural data. Our model exhibits exceptional predictive
capabilities and proves universally applicable to molecular and material
datasets. For instance, on the Tox21 dataset, we achieved an average precision
of 96%, surpassing the previously best result by 10%.
