The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage novel diffusion probabilistic (DP) models to denoise atomic coordinates rather than adopting the standard score matching approach in CDVAE. Our proposed DP-CDVAE model can reconstruct and generate crystal structures whose qualities are statistically comparable to those of the original CDVAE. Furthermore, notably, when comparing the carbon structures generated by the DP-CDVAE model with relaxed structures obtained from density functional theory calculations, we find that the DP-CDVAE generated structures are remarkably closer to their respective ground states. The energy differences between these structures and the true ground states are, on average, 68.1 meV/atom lower than those generated by the original CDVAE. This significant improvement in the energy accuracy highlights the effectiveness of the DP-CDVAE model in generating crystal structures that better represent their ground-state configurations.

翻译：晶体扩散变分自编码器（CDVAE）是一种利用分数匹配生成保持晶体对称性的真实晶体结构的机器学习模型。在本研究中，我们采用新型扩散概率（DP）模型对原子坐标进行去噪，而非沿用CDVAE中的标准分数匹配方法。我们提出的DP-CDVAE模型能够重建并生成质量在统计上与原始CDVAE相当的晶体结构。此外，值得注意的是，通过比较DP-CDVAE模型生成的碳结构与密度泛函理论计算获得的弛豫结构，我们发现DP-CDVAE生成的结构明显更接近各自对应的基态。这些结构与真实基态之间的能量差平均比原始CDVAE生成的结构低68.1 meV/原子。这一能量精度的显著提升凸显了DP-CDVAE模型在生成更能表征其基态构型的晶体结构方面的有效性。