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.
Authors
- Nikolaj Rønne
- Alán Aspuru‐Guzik (ORCID: https://orcid.org/0000-0002-8277-4434)
- Bjørk Hammer (ORCID: https://orcid.org/0000-0002-7849-6347)
Institutions
- Canadian Institute for Advanced Research (CA)
- University of Toronto (CA)
- Aarhus University (DK)
- Vector Institute (CA)
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
Funders
- Villum Fonden
- Danmarks Grundforskningsfond
- University of Toronto
- Canada First Research Excellence Fund
- Natural Resources Canada