[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127870-en":3,"doc-seo-127870-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127870,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods - Abstract - Introduction","Oxide perovskite materials of type ABO3 are central to applications such as solid-oxide fuel cell catalysis and light-absorbing solar photovoltaics, yet structural and electrostatic differences across A- and B-sites—especially when lanthanides are involved—remain difficult to verify experimentally or computationally. The study applies a data-driven machine learning workflow on the Li et al. (2018a) dataset, using nonparametric regressions, latent factor analysis, and principal component analysis. A nonparametric feature screening step supports robust high-dimensional regression.","arXiv :2303 .07441v3 [ cond-mat .mtrl-sci ] 9 Sep 2023  \nLinking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods  \nSampreeti Bhattacharya∗  \nand  \nArkaprava Roy†  \nSeptember 12, 2023  \nAbstract: Oxide perovskite materials of type ABO3 have a wide range of technological applications, such as catalysts in solid oxide fuel cells and as light-absorbing materials in solar photovoltaics. These materials often exhibit differential structural and electrostatic properties through their varied A-sites and B-sites especially if they are derived from lanthanides. Although, experimental and/or computational verification of these differences are often difficult and expensive. In this paper, we thus take a data-driven approach. Specifically, we run three analysis using the dataset Li et al. [2018a] applying advanced machine learning tools to perform nonparametric regressionsand also to produce data visualizations using latent factor analysis (LFA) and principal component analysis (PCA) . We also implement a nonparametric feature screening step while performing our high dimensional regression analysis, ensuring robustness in our results.  \nKeywords: Geometric descriptors, Materials Information, Perovskites, Stability.  \n1 Introduction  \nPerovskites are fascinating systems due to their intriguing physicochemical features and capacity to boost a wide range of electromagnetic and thermal processes. Owing to their ferromagnetic, ferroelectric, and piezoelectric characteristics, as well as ion conductivity, photocatalysis, and superconductivity applications, perovskite systems have a wide range of application [Hayward et al. , 2002, Jin et al., 2008, Yamada et al., 2008, Belik et al., 2016] . These materials are potently versatile in optoelectronic and spintronic applications, and their formation and stability is governed by atomistic metrics such as tolerance factor (t) and octahedral factor (µ), where t = (rA + rX ) / √2 (rB + rX ) and µ = (rB /rX ) respectively [Goldschmidt, 1926] . For stable perovskite compounds, these t values are in the range of 0.75 to 1.05, and µ values range from 0.00 to 0.15 . [Filip and Giustino, 2018, Zhao et al., 2021] . In addition to traditional geometric descriptors, significant advancements in stable perovskite predictions are also achieved utilizing Bartel et al. [2019] data-driven 1D descriptor, like τ providing 92% prediction performance for the ground-state-stable perovskites.  \n∗ Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA †Department of Biostatistics, University of Florida, Gainesville, FL, USA  \nIn ABO3 type oxide perovskite systems, the 12-fold coordinated A-sites are occupied by largesize alkali metal ions, alkaline earth metal ions, or lanthanide cations, while the 6-fold coordinated B-sites are occupied by transition metals [Wexler et al., 2021] . The chemical diversity in oxide perovskites makes the class of compound extremely tunable [Gopalakrishnan et al., 2020, Li et al. , 2022], at the same time raising an issue of incorrect identification of formable systems and the associated stability [Li et al., 2017] . In this work, we study a mixture of ABO3 perovskite systems [Bendersky et al., 2003, Liang et al., 2008, Nag and Shubha, 2014] . Varied A and B sites cations in ternary, quaternary and sextenary types of compositions yield different geometric environments. This further affects the connected octahedra and thereby structural features like Shannon radii, distance between A/B and oxides and other such attributes [Zhou, 2020] . The multitude of possible combinations in these metal cations leads to varying structural dependence on thermodynamic and electronic properties.  \nThe studies in Giaquinta and Zur Loye [1994], Goudochnikov and Bell [2007], Filip and Giustino [2018], Liang et al. [2020] demonstrate the efficacy of structural predictors in predicting the stability of perovskites. According t","cbCais0AMdxvjR7B","https://ap.wps.com/l/cbCais0AMdxvjR7B","pdf",7839099,1,22,"English","en",105,"# 1 Introduction\n## Perovskites and geometric descriptors\n## Lanthanide-rich ABO3 systems and structural diversity\n## Prior structural predictors and limitations\n## Geometric distortions, Glazer modes, and stability metrics","[{\"question\":\"Why is validating structural and electrostatic differences in lanthanide-derived perovskites difficult?\",\"answer\":\"Experimental and computational verification of differences across A-sites and B-sites can be difficult and expensive.\"},{\"question\":\"What dataset and analytical methods are used in the machine learning approach?\",\"answer\":\"The work uses the Li et al. (2018a) dataset and performs nonparametric regressions, along with latent factor analysis (LFA) and principal component analysis (PCA) for data visualization.\"},{\"question\":\"How does the study improve robustness in high-dimensional regression?\",\"answer\":\"It implements a nonparametric feature screening step while running the high-dimensional regression analysis.\"}]","Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods - Abstract - Introduction | PDF",1785942444,55,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"linking-stability-with-molecular-geometries-of-perovskites-and-lanthanide-richness-using-machine-learning-methods-abstract-introduction","",{"@graph":36,"@context":86},[37,54,69],{"@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/linking-stability-with-molecular-geometries-of-perovskites-and-lanthanide-richness-using-machine-learning-methods-abstract-introduction/127870/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is validating structural and electrostatic differences in lanthanide-derived perovskites difficult?","Question",{"text":76,"@type":77},"Experimental and computational verification of differences across A-sites and B-sites can be difficult and expensive.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and analytical methods are used in the machine learning approach?",{"text":81,"@type":77},"The work uses the Li et al. (2018a) dataset and performs nonparametric regressions, along with latent factor analysis (LFA) and principal component analysis (PCA) for data visualization.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study improve robustness in high-dimensional regression?",{"text":85,"@type":77},"It implements a nonparametric feature screening step while running the high-dimensional regression analysis.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]