Oxide Glasses with Enhanced IR Transmission

Glasses with high infrared (IR) transmission are critical for military applications. Current solutions based on chalcogenide glasse,s offer good optical performance but have unfavorable mechanical properties compared to many oxide glasses. Moreover, melting of ch,alcogenide glasses requires a vacuum or controlled atmosphere environment, which is prohibitive for large-scale manufacturing. In t,his project, we propose to develop new oxide glass compositions in the vanadate-germanate families that can achieve desired IR trans,mission while improving mechanical properties and facilitating larger-scale industrial processing. These glasses will be designed u,sing a hybrid modeling-experimental approach, in which topological constraint theory and statistical mechanical approaches are used,to predicted optimized glass network topologies in these mixed network former systems. Compositional candidates will be experimenta,lly synthesized and characterized for all optical, mechanical, and thermal properties of interest. The process conditions for the m,ost promising candidates will be optimized, and fibers will be drawn using the Mauro Glass Lab's single-filament fiber draw tower.,The combined set of glass composition-property data will be used to develop physics-informed machine learning models to optimize the, final glass composition, following the approach of 'decoding the glass genome' developed by the PI. This glass genome approach for, composition design has previously been used to develop highly successful glass compositions, including Corning Gorilla Glass produc,ts. Our project will build on this success and apply these approaches to a new family of mixed network former glasses, while simult,aneously developing a comprehensive understanding of the atomic-scale mechanisms governing the macroscopic properties of the glass.T,he project will be executed along four workstreams to: (A) develop models for the vanadate, germanate, and mixed network former glas,ses based on statistical mechanics and topological constraint theory and predict optimized glass network topologies; (B) synthesize,the glasses in the lab through crucible melting and thoroughly characterize the optical, mechanical, and thermal properties of the r,esulting samples; (C) draw the promising glass candidates into fibers and optimize the melting and forming processes; and (D) develo,p physics-informed machine learning-based models to optimize the final glass composition. These workstreams will involve a close co,upling of experimental and theoretical research using and extending approaches recently developed by the PI. The output from the pr,oject will include delivery of new optical glasses for IR transmission having improved mechanical properties and industrial processi,bility, as well as a deep fundamental understanding of the atomic-scale mechanisms for these improved properties. The project will,fund one Ph.D. student for three years to focus on the experimental and theoretical aspects of the project. Through this project, t,he student will be well-versed in both experimental and theoretical research, bridging the usual gap between experiment and theory., This research will advance fundamental science that is closely aligned with ONR goals as well as allow for specific DoD design inpu,t for targeted applications that facilitate current and adapting needs of the ONR.