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.

利用扩散概率模型 (DP 模型) 代替标准评分匹配，DP-CDVAE 模型能够重构和生成与原始 CDVAE 质量统计上可比的晶体结构，并且显著地提高了能量准确性，更好地表示晶体的基态构型。

扩散概率模型增强变分自编码器用于晶体结构生成建模

Diffusion probabilistic models enhance variational autoencoder for  crystal structure generative modeling

Predicting the structure of multi-protein complexes is a grand challenge in
biochemistry, with major implications for basic science and drug discovery.
Computational structure prediction methods generally leverage pre-defined
structural features to distinguish accurate structural models from less
accurate ones. This raises the question of whether it is possible to learn
characteristics of accurate models directly from atomic coordinates of protein
complexes, with no prior assumptions. Here we introduce a machine learning
method that learns directly from the 3D positions of all atoms to identify
accurate models of protein complexes, without using any pre-computed
physics-inspired or statistical terms. Our neural network architecture combines
multiple ingredients that together enable end-to-end learning from molecular
structures containing tens of thousands of atoms: a point-based representation
of atoms, equivariance with respect to rotation and translation, local
convolutions, and hierarchical subsampling operations. When used in combination
with previously developed scoring functions, our network substantially improves
the identification of accurate structural models among a large set of possible
models. Our network can also be used to predict the accuracy of a given
structural model in absolute terms. The architecture we present is readily
applicable to other tasks involving learning on 3D structures of large atomic
systems.

本文介绍了一种基于机器学习的方法，利用 3D 原子坐标来直接标识蛋白质复合物的准确模型，提高准确模型的识别，也可以用于绝对预测准确结构模型的方法。