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

Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships