Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence
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 net-work,we searched for novel materials with high Δn in the Materials Project database and discovered two new DUV bi-refringent candidates:NaYCO3F2 and SC1O2F,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 highΔn.
Research Center for Crystal Materials,State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions,Xinjiang Key Laboratory of Functional Crystal Materials,Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Urumqi 830011,China
Moscow Institute of Physics and Technology,9 Institutsky Lane,Dolgoprudny 141700,Russia
Emerging Technologies Research Center,XPANCEO,Internet City,Emmay Tower,Dubai,United Arab Emirates