摘要：Finding crystals with high birefringence(Δn),especially in deep-ultraviolet(DUV)regions,is important for developing polarization devices such as optical fiber *** materials are usually discovered using experimental techniques,which are costly and inefficient for a large-scale ***,we collected a database of crystal structures and their optical properties and trained atomistic line graph neural network to predict theirΔ*** estimate the level of confidence of the trained model on new data,D-optimality criterion was *** trained graph neural network,we searched for novel materials with highΔn in the Materials Project database and discovered two new DUV birefringent candidates:NaYCO_(3)F_(2) and SClO_(2)F,with highΔn values of 0.202 and 0.101 at 1064 nm,*** analysis reveals that strongly anisotropic units with various anions andπ-conjugated planar groups are beneficial for highΔn.