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

Abstract

Abstract Garnets 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 a substantial 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 potentially (meta-)stable garnet systems before performing systematic density-functional calculations to validate the predictions. We discover more than 600 ternary garnets with distances to the convex hull below 100 meV ⋅ atom−1. This includes sulfide, nitride, and halide garnets. We analyze their electronic structure and discuss the connection between the value of the electronic band gap and charge balance.