Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence

Abstract

Finding crystals with high birefringence (Δn), especially in deep-ultraviolet (DUV) regions, is important for developing polarization devices such as optical fiber sensors. Such materials are usually discovered using experimental techniques, which are costly and inefficient for a large-scale screening. Herein, we collected a database of crystal structures and their optical properties and trained atomistic line graph neural network to predict their Δn. To estimate the level of confidence of the trained model on new data, D-optimality criterion was implemented. Using trained graph neural network, we searched for novel materials with high Δn in the Materials Project database and discovered two new DUV birefringent candidates: NaYCO3F2 and SClO2F, with high Δn values of 0.202 and 0.101 at 1064 nm, respectively. Further analysis reveals that strongly anisotropic units with various anions and π-conjugated planar groups are beneficial for high Δn.