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Generative diffusion model for surface structure discovery
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DOI：10.1103/PhysRevB.110.235427.png)
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Abstract
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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 fixed substrate 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 forcefield for guided sampling of low-energy surface phases. An effective data-augmentation scheme for training the denoiser network is introduced to allow for scaling the structure 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 silver-oxide domain boundary of unprecedented size.
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3.7
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15.4W
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41.0W
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