Diffusion probabilistic models enhance variational autoencoder for
  crystal structure generative modeling
Teerachote Pakornchote Natthaphon Choomphon-anomakhun Sorrjit Arrerut Chayanon Atthapak Sakarn Khamkaeo Thiparat Chotibut Thiti Bovornratanaraks
The crystal diffusion variational autoencoder (CDVAE) is a machine learning
model that leverages score matching to generate realistic crystal structures
that preserve crystal symmetry. In this study, we leverage novel diffusion
probabilistic (DP) models to denoise atomic coordinates rather than adopting
the standard score matching approach in CDVAE. Our proposed DP-CDVAE model can
reconstruct and generate crystal structures whose qualities are statistically
comparable to those of the original CDVAE. Furthermore, notably, when comparing
the carbon structures generated by the DP-CDVAE model with relaxed structures
obtained from density functional theory calculations, we find that the DP-CDVAE
generated structures are remarkably closer to their respective ground states.
The energy differences between these structures and the true ground states are,
on average, 68.1 meV/atom lower than those generated by the original CDVAE.
This significant improvement in the energy accuracy highlights the
effectiveness of the DP-CDVAE model in generating crystal structures that
better represent their ground-state configurations.