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Position Papers of the 20th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 44

Detecting Spatial Ordering of Nanoparticles with Geometric Deep Learning

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DOI: http://dx.doi.org/10.15439/2025F8934

Citation: Position Papers of the 20th Conference on Computer Science and Intelligence Systems, M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 44, pages 55–60 ()

Full text

Abstract. Nanoparticle dispersion in heterogeneous catalysts plays a critical role in catalytic performance. We propose a robust and generalizable deep learning approach for classifying dispersion patterns in palladium on carbon (Pd/C) catalysts. Our method leverages graph neural networks (GNNs) that operate directly on particle location data extracted from scanning electron microscopy (SEM) images, avoiding reliance on image features that may introduce bias. It has an advantage over traditional image-based methods which risk overfitting to visual characteristics of the image unrelated to the spatial distribution of the nanoparticles. We validate the performance of our proposed GNN architecture on multiple Pd/C samples with distinct carbon support types, showing that our approach can reliably identify dispersion defects. The results highlight the potential of GNNs as a promising alternative for structure-based analysis and quality assessment of nanomaterial-based catalysts.

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