Equivariant Diffusion for Crystal Structure Prediction

TL;DR

EquiCSP introduces an equivariant diffusion model for crystal structure prediction that maintains symmetry properties, improves accuracy, and accelerates training convergence.

Contribution

The paper presents a novel equivariant diffusion model that addresses permutation and translation symmetries in CSP, a previously overlooked aspect.

Findings

Outperforms existing models in structure accuracy

Demonstrates faster convergence during training

Maintains periodic translation equivariance throughout

Abstract

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.