Li Zhelin, Mrad Rami, Jiao Runxian, Huang Guan, Shan Jun, Chu Shibing, Chen Yuanping
School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, P.R. China.
Jiangsu Engineering Research Center on Quantum Perception and Intelligent Detection of Agricultural Information, Zhenjiang 212013, China.
iScience. 2024 Dec 20;28(1):111659. doi: 10.1016/j.isci.2024.111659. eCollection 2025 Jan 17.
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 materials, 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 employed 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 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 conventional substitution or experience-based discovery.
长期以来，在材料设计中高效生成能量稳定的晶体结构一直是一项挑战，这主要是由于晶格中原子的排列方式极为复杂。为了便于发现稳定材料，我们提出了一个生成可合成材料的框架，该框架利用点云表示来编码复杂的结构信息。这个框架的核心是引入一个扩散模型作为其基础支柱。为了评估我们方法的有效性，我们用它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，我们通过生成材料展示了基于点云的晶体扩散（PCCD）的巨大潜力，强调了它们的可合成性。我们的研究通过生成式设计这一前沿途径，而非传统的替代或基于经验的发现，为材料设计和合成的进步做出了显著贡献。
