  Chalcogenide glasses possess several outstanding properties that enable
several ground breaking applications, such as optical discs, infrared cameras,
and thermal imaging systems. Despite the ubiquitous usage of these glasses, the
composition property relationships in these materials remain poorly understood.
Here, we use a large experimental dataset comprising approx 24000 glass
compositions made of 51 distinct elements from the periodic table to develop
machine learning models for predicting 12 properties, namely, annealing point,
bulk modulus, density, Vickers hardness, Littleton point, Youngs modulus, shear
modulus, softening point, thermal expansion coefficient, glass transition
temperature, liquidus temperature, and refractive index. These models, by far,
are the largest for chalcogenide glasses. Further, we use SHAP, a game theory
based algorithm, to interpret the output of machine learning algorithms by
analyzing the contributions of each element towards the models prediction of a
property. This provides a powerful tool for experimentalists to interpret the
models prediction and hence design new glass compositions with targeted
properties. Finally, using the models, we develop several glass selection
charts that can potentially aid in the rational design of novel chalcogenide
glasses for various applications.
