  The discovery of new catalysts is one of the significant topics of
computational chemistry as it has the potential to accelerate the adoption of
renewable energy sources. Recently developed deep learning approaches such as
graph neural networks (GNNs) open new opportunity to significantly extend scope
for modelling novel high-performance catalysts. Nevertheless, the graph
representation of particular crystal structure is not a straightforward task
due to the ambiguous connectivity schemes and numerous embeddings of nodes and
edges. Here we present embedding improvement for GNN that has been modified by
Voronoi tesselation and is able to predict the energy of catalytic systems
within Open Catalyst Project dataset. Enrichment of the graph was calculated
via Voronoi tessellation and the corresponding contact solid angles and types
(direct or indirect) were considered as features of edges and Voronoi volumes
were used as node characteristics. The auxiliary approach was enriching node
representation by intrinsic atomic properties (electronegativity, period and
group position). Proposed modifications allowed us to improve the mean absolute
error of the original model and the final error equals to 651 meV per atom on
the Open Catalyst Project dataset and 6 meV per atom on the intermetallics
dataset. Also, by consideration of additional dataset, we show that a sensible
choice of data can decrease the error to values above physically-based 20 meV
per atom threshold.

Materials Science;Machine Learning