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Garnets, known since the early stages of human civilization, have foundimportant applications in modern technologies including magnetorestriction,spintronics, lithium batteries, etc. The overwhelming majority ofexperimentally known garnets are oxides, while explorations (experimental ortheoretical) for the rest of the chemical space have been limited in scope. Akey issue is that the garnet structure has a large primitive unit cell,requiring an enormous amount of computational resources. To perform acomprehensive search of the complete chemical space for new garnets,we combinerecent progress in graph neural networks with high-throughput calculations. Weapply the machine learning model to identify the potential (meta-)stable garnetsystems before systematic density-functional calculations to validate thepredictions. In this way, we discover more than 600 ternary garnets withdistances to the convex hull below 100~meV/atom with a variety of physical andchemical properties. This includes sulfide, nitride and halide garnets. Forthese, we analyze the electronic structure and discuss the connection betweenthe value of the electronic band gap and charge balance.

Machine Learning guided high-throughput search of non-oxide garnets