Equivariant Diffusion for Crystal Structure Prediction
This work solves the problem of generating accurate crystal structures for materials science, with incremental improvements in symmetry handling.
The paper tackled the challenge of Crystal Structure Prediction by proposing EquiCSP, an equivariant diffusion-based generative model that addresses overlooked symmetry issues like lattice permutation and periodic translation equivariance, resulting in significantly more accurate structures and faster convergence compared to existing models.
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.