[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121955-en":3,"doc-seo-121955-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121955,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning guided high-throughput search of non-oxide garnets - Article","Machine learning guided high-throughput exploration identifies potentially stable non-oxide garnets beyond the predominantly known oxide chemical space. The approach combines graph neural networks to screen (meta-)stable ternary garnet systems with systematic density-functional calculations to validate predictions. Results reveal more than 600 ternary garnets with convex-hull distances below 100 meV/atom. The study analyzes electronic structure and links electronic band gaps to charge balance, supporting discovery of sulfide, nitride, and halide garnets.","[www.nature.com/npjcompumats](www.nature.com/npjcompumats)  \nARTICLE OPEN   \nMachine learning guided high-throughput search of non-oxide garnets  \nJonathan Schmidt 1, Hai-Chen Wang1, Georg Schmidt 1 and Miguel A. L. Marques 1 ✉  \n\n|  | 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 therest 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 |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| . .\u003Cbr>between the value of the electronic band gap and charge balance. |  |  |\n|  | npj Computational Materials (2023)9:63; [https://doi.org/10.1038/s41524-023-01009-4](https://doi.org/10.1038/s41524-023-01009-4) |  |\n|  |  |  |\n\nINTRODUCTION  \nGarnets can be found throughout the world in diverse geological environments, and have been known since prehistory mainly due to their use in jewelry as gemstones. They are also relatively hard minerals, a property that makes them useful for a series of industrial applications, such as in waterjet cutting or as abrasives.  \nGenerally, the garnets crystallize in a cubic structure (space group Ia3d) with chemical composition A3B2(B’C4)3, where the A atoms are located in the 24c dodecahedral sites, the B atoms are in the 16a octahedral, and B’ atoms occupy the 24d tetrahedral sites. In ternary garnets, B and B’ sites are occupied by the same chemical element. Around 19501–3 some rare-earth garnets, especially yttrium-based materials, started to attract attention. Those garnets have a general formula of RE3B2(BO4)3 where RE stands for rare-earth and B is a 3d magnetic transition metal (usually iron) or a group IIIA element. Among these, one of the most used ones is yttrium aluminum garnet (YAG), Y3Al2(AlO4)3, used as a synthetic simulant to diamond due to its high refractive index (> 1 .8)4. Doped YAGs with other rare-earth elements have found numerous applications as lasing media in modern medical laser devices5 or in tunable optical devices6–9.  \nOther important compounds, with interesting ferrimagnetic properties, are the rare-earth iron garnets (RE3Fe2(FeO4)3, RIG) . In the RIG structure, five Fe atoms occupy two different sublattices, and the antiferromagnetic coupling between sublattices and ferromagnetic coupling within the sublattice leads to a ferrimagnetic configuration. RIGs can display a rather high Curie temperature (around 560 K10), and some systems exhibit giant magnetorestriction 11. Moreover, RIGs materials have a band gap with values around 2 .6 to 2 .9 eV12, 13. Among these materials yttrium iron garnet (YIG) stands out because it has an exceptionally low Gilbert damping. YIG has first been used as bulk material in optical insulators, circulators, and Faraday rotators. Since the last two decades, YIG is also more and more frequently used as thin film material for spintronic applications14 because it allows the transmission of spin currents although being an insulator by itself. In recent years we have witnessed the attempt  \nto replace yttrium with lanthanides to increase the spin-orbit coupling and introduce even Dzyaloshinskii-Moriya interactions ","cbCaimFseilUkosM","https://ap.wps.com/l/cbCaimFseilUkosM","pdf",1507044,1,9,"English","en",105,"# Introduction\n## Garnet crystal structure and key applications\n## Rare-earth garnets and YAG/YIG relevance\n## Need for non-oxide garnets and challenges\n# Machine learning guided high-throughput workflow\n## Screening with graph neural networks\n## Density-functional validation\n# Results and electronic-structure analysis\n## Convex-hull stability of discovered ternary garnets\n## Band gap versus charge-balance relationships","[{\"question\":\"Why are non-oxide garnets less explored than oxides?\",\"answer\":\"Most garnet research focuses on oxides because they are easier to handle under ambient experimental conditions. Non-oxide exploration is also limited by the difficulty of computational high-throughput searches for the garnet prototype.\"},{\"question\":\"How does the study search for new non-oxide garnets?\",\"answer\":\"It first uses a graph neural network model to identify potentially stable (meta-)stable garnet systems, then applies systematic density-functional calculations to validate the predictions.\"},{\"question\":\"What materials and stability criterion are found in the results?\",\"answer\":\"The work discovers more than 600 ternary garnets, including sulfide, nitride, and halide compositions, with convex-hull distances below 100 meV per atom. The electronic structure is then analyzed to relate band gaps with charge balance.\"}]","Machine learning guided high-throughput search of non-oxide garnets - Article | PDF",1785808001,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-guided-high-throughput-search-of-non-oxide-garnets-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-guided-high-throughput-search-of-non-oxide-garnets-article/121955/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are non-oxide garnets less explored than oxides?","Question",{"text":75,"@type":76},"Most garnet research focuses on oxides because they are easier to handle under ambient experimental conditions. Non-oxide exploration is also limited by the difficulty of computational high-throughput searches for the garnet prototype.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study search for new non-oxide garnets?",{"text":80,"@type":76},"It first uses a graph neural network model to identify potentially stable (meta-)stable garnet systems, then applies systematic density-functional calculations to validate the predictions.",{"name":82,"@type":73,"acceptedAnswer":83},"What materials and stability criterion are found in the results?",{"text":84,"@type":76},"The work discovers more than 600 ternary garnets, including sulfide, nitride, and halide compositions, with convex-hull distances below 100 meV per atom. 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