Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model
CoRR(2024)
摘要
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.
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