Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Peichen Zhong, Xinzhe Dai, Bowen Deng, Gerbrand Ceder, Kristin A. Persson
| Summary:
We present Crystal Host-Guided Generation (CHGGen), a diffusion-based
framework for crystal structure prediction. Unconditional generation with
diffusion models demonstrates limited efficacy in identifying symmetric
crystals as the unit cell size increases. CHGGen addresses this limitation
through conditional generation with the inpainting method, which optimizes a
fraction of atomic positions within a predefined and symmetrized host
structure. We demonstrate the method on the ZnS-P$_2$S$_5$ and Li-Si chemical
systems, where the inpainting method generates a higher fraction of symmetric
structures than unconditional generation. The practical significance of CHGGen
extends to enabling the structural modification of crystal structures,
particularly for systems with partial occupancy, surface absorption and
defects. The inpainting method also allows for seamless integration with other
generative models, providing a versatile framework for accelerating materials
discovery.
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