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.

用点云表示的结构信息作为基础，我们提出了一种合成材料的框架来有效生成能量稳定的晶体结构，通过生成全新的材料来推动材料设计和合成的发展，而不是传统的替代或基于经验的发现。