  Due to their unique optical and electronic functionalities, chalcogenide
glasses are materials of choice for numerous microelectronic and photonic
devices. However, to extend the range of compositions and applications,
profound knowledge about composition-property relationships is necessary. To
this end, we collected a large quantity of composition-property data on
chalcogenide glasses from SciGlass database regarding glass transition
temperature ($T_g$), Young's modulus ($E$), coefficient of thermal expansion
(CTE), and refractive index ($n_D$). With these data, we induced predictive
models using three machine learning algorithms: Random Forest, K-nearest
Neighbors, and Classification and Regression Trees. Finally, the induced models
were interpreted by computing the SHAP (SHapley Additive exPlanations) values
of the chemical features, which revealed the key elements that significantly
impacted the tested properties and quantified their impact. For instance, Ge
and Ga increase $T_g$ and $E$ and decrease CTE (three properties that depend on
bond strength), whereas Se has the opposite effect. Te, As, Tl, and Sb increase
$n_D$ (which strongly depends on polarizability), whereas S, Ge, and P diminish
it. Knowledge about the effect of each element on the glass properties is
precious for semi-empirical compositional development trials or
simulation-driven formulations. The induced models can be used to design novel
chalcogenide glasses with required combinations of properties.
