Graph learning improved methanol-yield estimates for doped zirconia catalysts
Aarhus University researchers used a graph-based model to estimate energies on doped zirconia surfaces for CO₂ hydrogenation. In microkinetic calculations, those estimates produced methanol yields closer to density-functional-theory results than a workflow based on linear relations.
Event / data period: 2026-10-09
Published: · Updated:
The study evaluated aluminium, cadmium, gallium, indium and zinc dopants on ZrO₂(101) surfaces. Its WWL-GPR method represents surface structures as graphs combining local geometry and electronic features, then estimates adsorption energies of intermediates and transition states.
The model improved intermediate-energy predictions over thermodynamic scaling relations, while its transition-state estimates were competitive with Brønsted–Evans–Polanyi models. When the predicted energies were passed through a reaction network, estimated methanol yields tracked density-functional-theory-derived values more closely. These are computational screening results; the study did not demonstrate catalyst performance in experiments.
Data
Reported study result
Number of distinct dopants included in the computational screen; not a synthesized catalyst or measured production quantity.
| Dopant elements screened on ZrO₂ surfaces | 5 | Number of distinct dopants included in the computational screen; not a synthesized catalyst or measured production quantity. |
Methodology
The paper lists Al, Cd, Ga, In and Zn for ZrO₂(101) surfaces. Energy estimates were used in graph-model and microkinetic calculations.
Updated:
Subjects and places
Subject proposals
- Climate changeSuggested
- Subject suggested from the title or summary: CO₂arxiv.org ↗
- Science and technologySuggested
- Preserved editorial category/topic; broad candidate onlyarxiv.org ↗
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- Denmark · Place role: Coverage · Place type: Country or area · Location precision: RegionalEvidence
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