  Due to their excellent optical properties, glasses are used for various
applications ranging from smartphone screens to telescopes. Developing
compositions with tailored Abbe number (Vd) and refractive index (nd), two
crucial optical properties, is a major challenge. To this extent, machine
learning (ML) approaches have been successfully used to develop
composition-property models. However, these models are essentially black-box in
nature and suffer from the lack of interpretability. In this paper, we
demonstrate the use of ML models to predict the composition-dependent
variations of Vd and n at 587.6 nm (nd). Further, using Shapely Additive
exPlanations (SHAP), we interpret the ML models to identify the contribution of
each of the input components toward a target prediction. We observe that the
glass formers such as SiO2, B2O3, and P2O5, and intermediates like TiO2, PbO,
and Bi2O3 play a significant role in controlling the optical properties.
Interestingly, components that contribute toward increasing the nd are found to
decrease the Vd and vice-versa. Finally, we develop the Abbe diagram, also
known as the "glass veil", using the ML models, allowing accelerated discovery
of new glasses for optical properties beyond the experimental pareto front.
Overall, employing explainable ML, we discover the hidden compositional control
on the optical properties of oxide glasses.
