A ResNet18 model classifies surface chirality from atomic models at ~73% accuracy and from Fermi surface projections at ~99% accuracy, transferring to experimental synchrotron images after fine-tuning on two frames.
npj Computational Materials , author=
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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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Decoding Crystallographic Surface Chirality with Machine Learning: From Atomic Geometry to Fermi Surface Projections
A ResNet18 model classifies surface chirality from atomic models at ~73% accuracy and from Fermi surface projections at ~99% accuracy, transferring to experimental synchrotron images after fine-tuning on two frames.
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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.