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Teerachote Pakornchote1, Natthaphon Choomphon-Anomakhun1, Sorrjit Arrerut1

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A new diffusion probabilistic CDVAE model generates realistic crystal structures. This improved model produces structures closer to ground states, with significantly lower energy differences than the original CDVAE.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Crystal structure generation is crucial for materials discovery.
  • Existing models like crystal diffusion variational autoencoder (CDVAE) use score matching.
  • Symmetry preservation and accuracy in generated structures are key challenges.

Purpose of the Study:

  • To introduce a novel diffusion probabilistic CDVAE (DP-CDVAE) model.
  • To improve the accuracy of generated crystal structures, particularly their ground-state configurations.
  • To compare the performance of DP-CDVAE against the original CDVAE.

Main Methods:

  • Leveraging diffusion probabilistic (DP) models to denoise atomic coordinates.
  • Replacing the standard score matching approach in CDVAE with DP models.
  • Generating and evaluating crystal structures using the DP-CDVAE framework.

Main Results:

  • DP-CDVAE reconstructs and generates crystal structures with quality comparable to CDVAE.
  • DP-CDVAE generated carbon structures are significantly closer to their ground states.
  • Energy differences to true ground states are on average 68.1 meV/atom lower than CDVAE.

Conclusions:

  • The DP-CDVAE model effectively generates accurate crystal structures.
  • The use of diffusion probabilistic models enhances energy accuracy in generated structures.
  • DP-CDVAE shows promise for discovering materials with more stable configurations.