基于马尔可夫跳跃扩散的对称性破缺从头晶体生成
Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion
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- HES-SO Geneva（日内瓦高等教育学院）
- University of Geneva（日内瓦大学）
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中文总结 AI 辅助
该研究针对现有晶体生成模型难以捕捉全局对称性的问题，提出SbCD模型，通过马尔可夫跳跃扩散过程实现对称性破缺，在MP20和MPTS-52数据集上表现优于保对称性模型，为晶体生成建模提供新方案。
中文摘要 AI 辅助
生成晶体因在材料科学中的广泛应用而受到极大关注。然而，现有生成模型难以生成完整的晶体学规格，限制了其捕捉全局对称性和结构依赖关系的能力。特别是，当前最先进的方法仅能生成至位点对称性的晶体，且在生成过程中依赖于从经验分布中采样空间群。受物理学中“自发对称性破缺”（即晶体在外部条件下打破对称性）的启发，我们提出了一种新颖的基于扩散的框架，通过从最低对称性先验逆向生成完整结构规格。我们的方法利用马尔可夫跳跃扩散过程来建模这些对称性破缺动力学，使其能够以符合物理原理的方式遍历不同的空间群。我们的模型名为“对称性破缺晶体扩散”（Symmetry-breaking Crystal Diffusion, SbCD），引入了一种将空间群间转移明确纳入生成过程的原则性方法。在MP20和MPTS-52数据集上的从头生成实验中，SbCD的性能显著优于其保对称性的对应模型，为晶体材料的生成建模提供了有前景的视角。
英文摘要
Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their ability to capture global symmetry and structural dependencies. In particular, current state-of-the-art approaches generate crystals only up to site symmetries and rely on sampling space groups from empirical distributions during generation. Inspired by \emph{spontaneous symmetry breaking} in physics, where crystals break symmetries under external conditions, we propose a novel diffusion-based framework that generates full structure specifications by reversing from the lowest-symmetry priors. Our method leverages a Markovian jump-diffusion process to model these symmetry-breaking dynamics, enabling it to traverse different space groups in a physically motivated manner. Our model, dubbed \emph{Symmetry-breaking Crystal Diffusion} (SbCD), introduces a principled approach to explicitly incorporate inter-space-group transitions into the generative process. In de novo generation experiments on MP20 and MPTS-52, SbCD outperforms its symmetry-preserving counterpart by a substantial margin, offering a promising perspective for generative modeling of crystalline materials.
