基于对称性的生成模型可高效设计新型稳定晶体。
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
- 利用对称性感知的贝叶斯流网络建模晶体空间群分布。
- 生成速度比现有方法快至少50倍，且能生成稳定结构。
- 适合需要定制性能晶体材料的研究者使用。
新晶态材料的发现对科学与技术进步至关重要。传统试错方法因搜索空间巨大而效率低下。近年来，机器学习生成模型通过融入结构对称性，实现了对稳定材料的预测，并能根据所需性质进行条件生成。本文提出一种新的对称性感知贝叶斯流网络（SymmBFN），可准确复现实验观测晶体中的空间群分布。SymmBFN显著提升生成效率，生成稳定结构的速度至少比当前最优方法快50倍。此外，我们展示了其在性质条件生成方面的能力，可实现具有定制性能材料的设计。研究结果表明，贝叶斯流网络是加速晶态材料发现的有效工具。
原文摘要 · Abstract (English)
The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast search space. Recent advancements in machine learning have enabled generative models to predict new stable materials by incorporating structural symmetries and to condition the generation on desired properties. In this work, we introduce SymmBFN, a novel symmetry-aware Bayesian Flow Network (BFN) for crystalline material generation that accurately reproduces the distribution of space groups found in experimentally observed crystals. SymmBFN substantially improves efficiency, generating stable structures at least 50 times faster than the next-best method. Furthermore, we demonstrate its capability for property-conditioned generation, enabling the design of materials with tailored properties. Our findings establish BFNs as an effective tool for accelerating the discovery of crystalline materials.
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