  A multitask deep neural network model was trained on more than 218k different
glass compositions. This model, called GlassNet, can predict 85 different
properties (such as optical, electrical, dielectric, mechanical, and thermal
properties, as well as density, viscosity/relaxation, crystallization, surface
tension, and liquidus temperature) of glasses and glass-forming liquids of
different chemistries (such as oxides, chalcogenides, halides, and others). The
model and the data used to train it are available in the GlassPy Python module
as free and open source software for the community to use and build upon. As a
proof of concept, GlassNet was used with the MYEGA viscosity equation to
predict the temperature dependence of viscosity and outperformed another
general purpose viscosity model available in the literature (ViscNet) on unseen
data. An explainable AI algorithm (SHAP) was used to extract knowledge
correlating the input (physicochemical information) and output (glass
properties) of the model, providing valuable insights for glass manufacturing
and design. It is hoped that GlassNet, with its free and open source nature,
can be used to enable faster and better computer-aided design of new
technological glasses.
