基于流生成模型的拓扑分层材料发现

中文摘要 AI 辅助

晶体结构的准确生成是发现用于极端环境（如航空航天、增材制造和聚变能源系统）高性能材料的基础。尽管生成建模已成为晶体设计的一种有前景的方法，但其性能仍受限于复杂的晶体结构和多样的化学成分。在本工作中，我们开发了UFO-MGen，一种通用的基于流的生成模型，它学习Wyckoff表示的拓扑特征，并利用这些信息在广阔的结构和化学空间中准确生成晶体。与最先进的生成模型相比，UFO-MGen在严格的多稳定性评估框架下实现了最高的晶体生成成功率、最高的SUN（稳定、独特、新颖）率，以及先前模型未曾报道的显著外推能力。此外，我们为UFO-MGen实现了一个微调模块，用于受性质约束的晶体生成，从而实现对目标性质的逆向材料设计。UFO-MGen为加速材料发现开辟了新途径，并为通用材料智能提供了基础。

英文摘要

Accurate generation of crystal structures is the foundation to the discovery of high-performance materials for extreme-environment applications, such as aerospace, additive manufacturing, and fusion energy systems. Although generative modeling has emerged as a promising approach for crystal design, its performance remains limited by the complex crystal structures and diverse chemical compositions. In this work, we develop UFO-MGen, a universal flow-based generative model that learns topological features of Wyckoff representations and leverages this information to accurately generate crystals across vast structural and chemical spaces. Compared with state-of-the-art generative models, UFO-MGen achieves the highest crystal generation success rate under a rigorous multi-stability evaluation framework, the highest SUN (stable, unique, novel) rate, and a remarkable extrapolation capability that has not been reported by previous models. Furthermore, a fine-tuning module is implemented to UFO-MGen for property-constrained crystal generation, enabling the inverse materials design toward target properties. The UFO-MGen opens a new avenue for accelerated materials discovery and providing a foundation for universal materials intelligence.

发表机构

• University of Alabama（阿拉巴马大学）
• Alabama Materials Institute（阿拉巴马材料研究所）

机构由 AI 辅助整理，请以论文原文为准。