Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models

TL;DR

This paper introduces WyckoffDiff-Adaptor, a diffusion model that enables precise symmetry-aware conditional generation of crystal structures, improving material discovery by controlling space group symmetry and physical properties.

Contribution

The paper presents a novel diffusion-based architecture that effectively incorporates symmetry constraints for conditional crystal structure generation, addressing limitations of previous models.

Findings

Successfully generated stable structures with specified properties.

Achieved accurate symmetry control in generated crystal structures.

Demonstrated potential for discovering new materials with targeted features.

Abstract

The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.