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

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Schmidt, Jonathan</dc:creator>
  <dc:creator>Wang, Hai-Chen</dc:creator>
  <dc:creator>Schmidt, Georg</dc:creator>
  <dc:creator>L. Marques, Miguel A.</dc:creator>
  <dc:description>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. This record includes the results of 15742 vasp calculations of the ternary garnets investigated. All calculations were performed using the PBE functional.</dc:description>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
  <dc:subject>density-functional theory</dc:subject>
  <dc:title>Machine learning guided high-throughput search of non-oxide garnets</dc:title>