Crystal Structure Prediction by Joint Equivariant Diffusion

TL;DR

This paper introduces DiffCSP, a novel equivariant diffusion model that effectively predicts crystal structures by jointly generating lattice and atom coordinates, leveraging fractional coordinates to improve accuracy and efficiency.

Contribution

The paper presents DiffCSP, the first diffusion model for crystal structure prediction that incorporates symmetry invariance and uses fractional coordinates for enhanced generation quality.

Findings

DiffCSP outperforms existing CSP methods in accuracy.

DiffCSP achieves lower computational costs than DFT-based approaches.

DiffCSP extends effectively to ab initio crystal generation.

Abstract

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures -- the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation.