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Atomic-Scale Polycrystal Reconstruction Using Diffusion Models
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AI
DOI：10.2320/matertrans.MT-M2025159.png)
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Abstract
En 中文
A method combining a variational autoencoder (VAE) and a denoising diffusion probabilistic model (DDPM) is proposed to efficiently compress and reconstruct complex, large-scale atomic coordinates, such as those in polycrystals. Using the atomic coordinates of Ni polycrystalline, we show that the combination of the VAE and DDPM improves reconstruction accuracy compared with the VAE alone. Moreover, the proposed method can reproduce the time evolution of atomic coordinates from MD simulations, even when trained on a single polycrystal at a specific time. In addition, the deviation in the number of atoms from the original coordinates was found to be small across all subregions, including those with various facets and grain boundaries, demonstrating robustness of the model proposed in this study.
Keywords:
deep generative model
diffusion model
polycrystal
grain boundary
molecular dynamics