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

TL;DR

This study employs machine learning and high-throughput calculations to discover over 600 new non-oxide garnets, expanding the known chemical space and analyzing their electronic properties.

Contribution

It introduces a combined approach of graph neural networks and density-functional calculations to efficiently identify and validate new garnet materials beyond oxides.

Findings

Discovered over 600 potential stable garnets including sulfide, nitride, and halide variants.

Identified correlations between electronic band gaps and charge balance in garnets.

Demonstrated the effectiveness of machine learning in accelerating materials discovery.

Abstract

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.