Boosting heterogeneous catalyst discovery by structurally constrained deep learning models

Materials Today Chemistry. 2023.

Буденный С. А., Korovin A. N., Humonen I. S., Samtsevich A. I., Eremin R. A., Vasilev A. I., Lazarev V. D.

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 open new opportunity to significantly extend scope for
modeling novel high-performance catalysts. Nevertheless, the graph representation of a 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 graph neural networks
that has been modified by Voronoi tessellation and is able to predict the energy of catalytic systems
within the Open Catalyst Project dataset. The enrichment of the graph was calculated via Voronoi
tessellation, and the corresponding contact solid angles and types (direct/indirect) were considered as
edges’ features, and Voronoi volumes were used as node characteristics. The auxiliary approach was
enriching node representation by intrinsic atomic properties (electronegativity, period, and group po-
sition). The proposed modifications allowed us to improve the mean absolute error of the original model,
and the final error equals to 651 meV on the Open Catalyst Project dataset and 6 meV/atom on the
intermetallics dataset. Also, by the consideration of an additional dataset, we show that a sensible choice
of data can decrease the error to values below a physically-based 20 meV/atom threshold.

Keywords: deep machine learning