WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry

• URL: http://arxiv.org/abs/2502.06485v1
• Date: Mon, 10 Feb 2025 14:04:23 GMT
• Title: WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry
• Authors: Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten,
• Abstract summary: We propose a generative model, Wyckoff Diffusion, which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation.
• Score: 10.650073214237207
• License:
• Abstract: 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, Fr\'echet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation.

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