  Garnets, known since the early stages of human civilization, have found
important applications in modern technologies including magnetorestriction,
spintronics, lithium batteries, etc. The overwhelming majority of
experimentally known garnets are oxides, while explorations (experimental or
theoretical) for the rest of the chemical space have been limited in scope. A
key issue is that the garnet structure has a large primitive unit cell,
requiring an enormous amount of computational resources. To perform a
comprehensive search of the complete chemical space for new garnets,we combine
recent progress in graph neural networks with high-throughput calculations. We
apply the machine learning model to identify the potential (meta-)stable garnet
systems before systematic density-functional calculations to validate the
predictions. In this way, we discover more than 600 ternary garnets with
distances to the convex hull below 100~meV/atom with a variety of physical and
chemical properties. This includes sulfide, nitride and halide garnets. For
these, we analyze the electronic structure and discuss the connection between
the value of the electronic band gap and charge balance.
