用对称性生成晶体，突破传统模型的原子位置限制。
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
- 基于对称性编码的晶体表示，构建离散生成框架
- 生成速度快，且天然满足晶体对称性约束
- 新指标评估生成材料对称性，适合材料发现研究
晶体材料通常具有高度对称性，但多数生成模型未考虑对称性，仅自由建模每个原子的位置与元素。我们提出一种生成模型——WyckoffDiff，通过编码完整对称性的晶体结构表示，实现基于对称性的晶体生成。该方法设计了新型神经网络架构，使对称性表示可嵌入离散生成框架。模型不仅从结构上保证对称性，其离散特性也支持快速生成。我们还提出了新度量指标——弗雷歇威诺弗距离（Fréchet Wrenformer Distance），用于捕捉生成材料的对称特征，并在近期晶体生成模型上进行了基准测试。作为概念验证，我们利用WyckoffDiff在热力学稳定性的凸包下方寻找新材料。
原文摘要 · Abstract (English)
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.
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