Efficiently generating energetically stable crystal structures has long been a challenge in material design, primarily due to the immense arrangement of atoms in a crystal lattice. To facilitate the discovery of stable material, we present a framework for the generation of synthesizable materials, leveraging a point cloud representation to encode intricate structural information. At the heart of this framework lies the introduction of a diffusion model as its foundational pillar. To gauge the efficacy of our approach, we employ it to reconstruct input structures from our training datasets, rigorously validating its high reconstruction performance. Furthermore, we demonstrate the profound potential of Point Cloud-Based Crystal Diffusion (PCCD) by generating entirely new materials, emphasizing their synthesizability. Our research stands as a noteworthy contribution to the advancement of materials design and synthesis through the cutting-edge avenue of generative design instead of the conventional substitution or experience-based discovery.

翻译：高效生成能量稳定的晶体结构长期以来一直是材料设计中的挑战，这主要源于晶格中原子的巨大排列空间。为促进稳定材料的发现，我们提出了一种可合成材料的生成框架，该框架利用点云表示来编码复杂的结构信息。该框架的核心是引入扩散模型作为其基础支柱。为评估方法的有效性，我们采用该模型从训练数据集中重建输入结构，严格验证了其高重建性能。此外，我们通过生成全新材料（强调其可合成性）展示了基于点云的晶体扩散模型（PCCD）的巨大潜力。本研究通过生成设计这一前沿途径（而非传统的替代或基于经验的发现方式），为材料设计与合成的发展做出了显著贡献。