[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126563-en":3,"doc-seo-126563-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126563,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods","Oxide perovskite materials ABO3 serve in solid oxide fuel cells as catalysts and in solar photovoltaics as light-absorbing layers, yet lanthanide-derived versus non-lanthanide-derived A- and B-site choices can produce meaningful structural and electrostatic differences that remain difficult to verify experimentally or computationally. This study uses a data-driven machine learning workflow on the Li, Jacobs, and Morgan dataset, applying nonparametric regression, latent factor analysis and principal component analysis for visual insight, and high-dimensional feature screening to maintain robustness.","arXiv :2303 .07441v2 [ cond-mat .mtrl-sci ] 21 Apr 2023  \nLinking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods  \nSampreeti Bhattacharya∗  \nand  \nArkaprava Roy†  \nApril 24, 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 di􀀋erential structural and electrostatic properties through lanthanide or non-lanthanide derived A- and B- sites. Although, experimental and/or computational veri􀀌cation of these di􀀋erences are often di􀀎cult. In this paper, we thus take a data-driven approach. Speci􀀌cally, we run three analysis using the dataset Li, Jacobs, and Morgan [2018a] applying advanced machine learning tools to perform nonparametric regressions and 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, Cussen, Claridge, Bieringer, Rosseinsky, Kiely, Blundell, Marshall, and Pratt, 2002, Jin, Zhou, Goodenough, Liu, Zhao, Yang, Yu, Yu, Katsura, Shatskiy, et al. , 2008, Yamada, Takata, Hayashi, Shinohara, Azuma, Mori, Muranaka, Shimakawa, and Takano, 2008, Belik, Glazkova, Katsuya, Tanaka, Sobolev, and Presniakov, 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 (􀀖), where t = (rA + rX ) = p 2 (rB + rX ) and 􀀖 = (rB =rX ) respectively [Goldschmidt, 1926] . For stable perovskite compounds, these t values are  \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 the range of 0.75 to 1.05, and 􀀖 values range from 0.00 to 0.15 . [Filip and Giustino, 2018, Zhao, Gao, Li, Qian, Shen, Wang, Shen, Hu, Dong, Huang, et al., 2021] .  \nIn ABO3 type oxide perovskite systems, the 12-fold coordinated A-sites are occupied by large size alkali metal ions, alkaline earth metal ions or lanthanide cations, while the 6-fold coordinated Bsites, are occupied by transition metals [Wexler, Gautam, Stechel, and Carter, 2021] . The chemical diversity in oxide perovskites makes the class of compound extremely tunable [Gopalakrishnan, Sebastian, and Ahn, 2020, Li, Lin, Liu, Hu, Cao, Chen, and Xing, 2022], at the same time raising an issue of incorrect identi􀀌cation of formable systems and the associated stability [Li, Wang, Deschler, Gao, Friend, and Cheetham, 2017] . In this work, we study mixture of ABO3 perovskite systems [Bendersky, Greenblatt, and Chen, 2003, Liang, Tang, Shao, Li, Zeng, and Zheng, 2008, Nag and Shubha, 2014], where the A+ sites metal cations are in ternary, quaternary and sextenary types of environment attributed to the B+ site cations, and connected individual octahedra [Zhou, 2020] . The multitude of possible combinations in these metal cations leads to varying structural dependence on thermodynamic and electronic properties. Accordingly, the two most fundamental metrics that link the stability of geometries based on the spatial arrangement of atoms in a lattice are the octahedral factor and the Goldschmidt tolerance factor.  \nThe studies i","cbCainNp5uU2krsr","https://ap.wps.com/l/cbCainNp5uU2krsr","pdf",9066290,3,1,22,"English","en",105,"# Introduction\n## Structural descriptors and stability metrics\n## Machine-learning data-driven analysis approach\n## Geometric modes and electrostatic effects","[{\"question\":\"What problem does the paper address about ABO3 perovskites?\",\"answer\":\"It addresses how lanthanide- versus non-lanthanide-derived A- and B-site choices lead to structural and electrostatic differences, which are often hard to validate experimentally or computationally.\"},{\"question\":\"Which machine learning analyses are used in the study?\",\"answer\":\"The study runs three analyses using the Li, Jacobs, and Morgan dataset, including nonparametric regressions and visualizations via latent factor analysis (LFA) and principal component analysis (PCA), alongside nonparametric feature screening.\"},{\"question\":\"Why are geometric descriptors and tolerance factors important here?\",\"answer\":\"Perovskite stability and formability are linked to atomistic geometry metrics such as the Goldschmidt tolerance factor and the octahedral factor, which connect lattice spatial arrangements to electronic and structural behavior.\"}]","Linking stability with molecular geometries of perovskites and lanthanide richness using machine learning methods | PDF",1785933344,55,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"linking-stability-with-molecular-geometries-of-perovskites-and-lanthanide-richness-using-machine-learning-methods","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/linking-stability-with-molecular-geometries-of-perovskites-and-lanthanide-richness-using-machine-learning-methods/126563/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","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},"What problem does the paper address about ABO3 perovskites?","Question",{"text":76,"@type":77},"It addresses how lanthanide- versus non-lanthanide-derived A- and B-site choices lead to structural and electrostatic differences, which are often hard to validate experimentally or computationally.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning analyses are used in the study?",{"text":81,"@type":77},"The study runs three analyses using the Li, Jacobs, and Morgan dataset, including nonparametric regressions and visualizations via latent factor analysis (LFA) and principal component analysis (PCA), alongside nonparametric feature screening.",{"name":83,"@type":74,"acceptedAnswer":84},"Why are geometric descriptors and tolerance factors important here?",{"text":85,"@type":77},"Perovskite stability and formability are linked to atomistic geometry metrics such as the Goldschmidt tolerance factor and the octahedral factor, which connect lattice spatial arrangements to electronic and structural behavior.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]