提出可保持晶体对称性的扩散模型，提升结构生成准确率与训练速度。
Equivariant Diffusion for Crystal Structure Prediction
- 设计新去噪算法，确保晶格排列、旋转和周期平移的对称性不变
- 生成结构精度显著超越现有模型，训练收敛更快
- 适合需高精度晶体结构生成的研究者使用
针对晶体结构预测（CSP）问题，现有基于扩散模型的方法虽已广泛应用，但对扩散过程中排列、旋转及周期平移等对称性保持不充分。本文提出EquiCSP，一种新型等变扩散生成模型。不仅解决以往模型中晶格排列对称性被忽略的问题，还设计了一种独特的去噪算法，在训练与推理阶段严格维持周期平移等变性。实验表明，EquiCSP在生成准确结构方面显著优于现有模型，并展现出更快的训练收敛速度。
原文摘要 · Abstract (English)
In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process.
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