Generative diffusion model for surface structure discovery

The generative diffusion method is adapted for use in connection with surface structure determination. The authors introduce here the concepts required to train a diffusion model on structures from small-cell density functional theory calculations. Using the diffusion model to generate large-cell surface structures, the authors demonstrate that highly plausible structures result. The efficiency of generating low-energy structures is compared to that of the random structure search method. Finally, using the diffusion method, a structure for a Ag-based surface-oxide grain boundary is proposed.

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Publication Details

Journal
Physical review. B./Physical review. B
Published
2024-12-24
DOI
https://doi.org/10.1103/physrevb.110.235427
Citations
20
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.34

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article

Generative diffusion model for surface structure discovery

Nikolaj Rønne, Alán Aspuru‐Guzik, Bjørk Hammer
20 citations
Physical review. B./Physical review. B
Machine Learning in Materials Science
2.34
article

Generative diffusion model for surface structure discovery

Nikolaj Rønne, Alán Aspuru‐Guzik, Bjørk Hammer
article en
20 citations

Abstract

The generative diffusion method is adapted for use in connection with surface structure determination. The authors introduce here the concepts required to train a diffusion model on structures from small-cell density functional theory calculations. Using the diffusion model to generate large-cell surface structures, the authors demonstrate that highly plausible structures result. The efficiency of generating low-energy structures is compared to that of the random structure search method. Finally, using the diffusion method, a structure for a Ag-based surface-oxide grain boundary is proposed.

Physical review. B./Physical review. BVol. 110(23)
Canadian Institute for Advanced Research (CA), University of Toronto (CA), Aarhus University (DK), Vector Institute (CA)
Villum Fonden, Danmarks Grundforskningsfond, University of Toronto, Canada First Research Excellence Fund, Natural Resources Canada
Climate action
Openalex Percentile: Top 7%
Machine Learning in Materials Science
2.34
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