Generative diffusion model for surface structure discovery
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915111402209280 |
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| author | Rønne, Nikolaj Aspuru-Guzik, Alán Hammer, Bjørk |
| author_facet | Rønne, Nikolaj Aspuru-Guzik, Alán Hammer, Bjørk |
| contents | We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion alongside a force-field for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser-network is proposed to scale generation far beyond structure sizes represented in the training data. We showcase the generative model by investigating multiple surface systems and propose an atomistic structure model for a previously unknown silver-oxide domain-boundary of unprecedented size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative diffusion model for surface structure discovery Rønne, Nikolaj Aspuru-Guzik, Alán Hammer, Bjørk Computational Physics Materials Science We present a generative diffusion model specifically tailored to the discovery of surface structures. The generative model takes into account substrate registry and periodicity by including masked atoms and $z$-directional confinement. Using a rotational equivariant neural network architecture, we design a method that trains a denoiser-network for diffusion alongside a force-field for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser-network is proposed to scale generation far beyond structure sizes represented in the training data. We showcase the generative model by investigating multiple surface systems and propose an atomistic structure model for a previously unknown silver-oxide domain-boundary of unprecedented size. |
| title | Generative diffusion model for surface structure discovery |
| topic | Computational Physics Materials Science |
| url | https://arxiv.org/abs/2402.17404 |