| Abstract Scope |
Despite the use of glasses for more than 2000 years, the composition-structure-property relationships in these materials remain elusive. Here, we discuss how the improved glasses can be designed for nuclear waste immobilization. Specifically, we focus on three aspects namely, (i) ML-based property prediction, (ii) physics-informed ML for viscosity prediction, and (iii) information extraction from the literature. First, we discuss how ML models can be used for predicting electrical, and mechanical properties. Second, we show the use of physics-informed ML for predicting viscosity, which combines the MYEGA equation with ML to predict the viscosity of unknown glasses. Further, the ML models are used as surrogates along with constrained optimization to identify new potential glass compositions. Finally, we discuss how natural language processing can be used to extract information from the literature regarding nuclear waste immobilization, which can be effectively used for accelerating the discovery of new glasses. |