Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866908562249220096 |
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| author | Li, Zhelin Mrad, Rami Jiao, Runxian Huang, Guan Shan, Jun Chu, Shibing Chen, Yuanping |
| author_facet | Li, Zhelin Mrad, Rami Jiao, Runxian Huang, Guan Shan, Jun Chu, Shibing Chen, Yuanping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13192 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model Li, Zhelin Mrad, Rami Jiao, Runxian Huang, Guan Shan, Jun Chu, Shibing Chen, Yuanping Artificial Intelligence Materials Science Machine Learning Computational Physics 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. |
| title | Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model |
| topic | Artificial Intelligence Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2401.13192 |