WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
This work addresses the problem of generating realistic crystalline materials for the field of materials science, which is crucial for discovering new materials with desired properties.
The authors tackled the problem of generating crystalline materials with symmetry using a generative diffusion model, WyckoffDiff, and achieved fast generation while respecting symmetry by construction. WyckoffDiff was used to find new materials below the convex hull of thermodynamical stability.
Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry, and we design a novel neural network architecture which enables using this representation inside a discrete generative model framework. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation. We additionally present a new metric, Fréchet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation. As a proof-of-concept study, we use WyckoffDiff to find new materials below the convex hull of thermodynamical stability.