CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.
materialscloud:2020.0026/v1 , year=
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deCIFer trains an autoregressive LM on 2.3 million structures with synthetic PXRD noise to generate CIF files, reporting 94% structural match rate on synthetic inorganic test sets.
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Latent Diffusion Pretraining for Crystal Property Prediction
CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.
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deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models
deCIFer trains an autoregressive LM on 2.3 million structures with synthetic PXRD noise to generate CIF files, reporting 94% structural match rate on synthetic inorganic test sets.