Symmetry-informed graph neural networks for carbon dioxide isotherm and adsorption prediction in aluminum-substituted zeolites
SymGNN leverages crystal symmetries to improve adsorption predictions in zeolites. By encoding symmetry-aware features, the model achieves accurate isotherm and heat of adsorption predictions, even for unseen topologies.
Authors
- José Manuel Vicent‐Luna (ORCID: https://orcid.org/0000-0001-8712-5591)
- Vlado Menkovski (ORCID: https://orcid.org/0000-0001-5262-0605)
- Sofı́a Calero (ORCID: https://orcid.org/0000-0001-9535-057X)
- Marko Petković (ORCID: https://orcid.org/0009-0000-4918-6027)
- Elīza Beate Dinne
Institutions
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta02482j
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52