Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling.

ABSTRACT: 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

SUBMITTER: Pakornchote T 

PROVIDER: S-EPMC10787843 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature