[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128215-en":3,"doc-seo-128215-105":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},128215,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine learning approach to the possible synergy between co-doped elements in the case of LiFePO4/C","This study investigates the synergistic effects produced by the co-doping of several components in the LFP/C structure. A dataset was built from existing literature on single-element doped LiFePO4 structures, and intrinsic/extrinsic descriptors were screened using Pearson correlation. Random Forest and Gaussian Process Regression were trained on the optimized feature set, evaluated for predictive power, and used to estimate synergy via comparison of actual vs superimposed expected specific discharge capacities. Experimental synthesis and characterization of LiYxNdyFe1-x-yPO4/C samples were performed using solid-state methods and SEM, TEM, CV, EIS, and GD. ","Journal of Alloys and Compounds 1034 (2025) 181316  \nContents lists available at ScienceDirect  \nJournal of Alloys and Compounds  \njournal [homepage:](homepage: www.elsevier.com/locate/jalcom)[ www.elsevier.com/locate/jalcom](homepage: www.elsevier.com/locate/jalcom)  \n| Machine learning approach to the possible synergy between co-doped elements in the case of LiFePO4/C\u003Cbr>Z.M.S. Elbarbary a,b, Priya A. Hoskeri c,*, Ali A. Javidparvard,*, Mohammed M. Alammar a,b, Amuthakkannan Rajakannu e, Theodore Azemtsop Manfof,*\u003Cbr>a Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61421, Saudi Arabia b Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi Arabia\u003Cbr>c VTU Research Center, Department of Physics, Dayanand Sagar College of Engineering, Kumarswamy Layout, Bangalore 560 078, India d School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran e Department of Mechanical and Industrial Engineering, National University of Science and Technology, Muscat, Oman\u003Cbr>f Department of Electrical Engineering and Energy Technology, School of Technology and Innovations, University of Vaasa, Wolffintie 32, Vaasa 65200, Finland |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Co-doping\u003Cbr>Li-ion batteries Machine Learning Synergic effects LFP |  | This study investigates the synergistic effects produced by the co-doping of several components in the LFP/C structure. To execute this work, a dataset was initially created from the existing literature, encompassing information on doped LFP structures by a singular element. Numerous intrinsic and extrinsic characteristics, such as atomic number, valence, relative variations in atomic and ionic radii of Fe and Li, electronegativity, molar percentage of dopant, and C-rate, were evaluated. The optimal selection of features leading to satisfactory model training was achieved by analyzing the Pearson correlation coefficient factors. Subsequently, two machine learning algorithms (i.e., Random Forest and Gaussian Process Regression) were trained using the optimized feature set. The two models were evaluated, and the model with superior predictive power was chosen for further study. An analysis of the synergistic effect of two co-dopants was conducted by comparing the actual specific discharge capacities with the expected values derived from the superimposition of the machine learning predictions. Ultimately, experimental validation was conducted by synthesizing several unique LiYxNdyFe1-x-yPO4/C (Nd = 0.06, 0.02 \u003CY\u003C0.08) samples using solid-state methods. The synthesized powders underwent relevant testing, including SEM, TEM, CV, EIS, and GD. Finally, based on the best ML scheme developed and experimental results, another ML scheme was developed to analyze the possible synergic effects that co-dopants may exhibit regarding the specific discharge capacity of co-doped LFP structures. |  |\n\n1. Introduction  \nFor the chemical storage of electricity, there are two categories of batteries: (a) primary (non-rechargeable) batteries and (b) secondary (rechargeable) ones. Commercially produced secondary batteries include LA (lead-acid), Ni-Cd (nickel-cadmium), LiMH (nickel-metal hydride), LiBs and SiBs (sodium-, and lithium-ion batteries) [1–7]. Cost-effective and secure energy storage is crucial for the effective implementation of electronic and electrical appliances and equipment, with rechargeable batteries emerging as a prominent solution [8,9], especially within renewable energy systems. LiBs stand out as the most efficient and versatile technology, providing exceptional energy and current density, elevated voltage, and extended life cycles that can  \nsurpass 1000 cycles. In this regard, they have emerged as the favored alternative to outdated technologies like LA and Ni-Cd. Recent advancements, incl","cbCais5obzJGQwx5","https://ap.wps.com/l/cbCais5obzJGQwx5","pdf",24997537,3,1,21,"English","en",105,"# Introduction\n## Background on rechargeable batteries and Li-ion cathodes\n# Study objective and materials focus\n# Dataset construction and feature selection\n# Machine learning models and evaluation\n## Random Forest\n## Gaussian Process Regression\n# Synergy analysis for co-dopants\n# Experimental validation and characterization\n## Sample synthesis\n## Characterization methods","[{\"question\":\"How was the dataset for co-doping synergy modeling constructed?\",\"answer\":\"A dataset was created from existing literature, containing information on doped LFP structures with a single element dopant before extending to co-doping analysis.\"},{\"question\":\"Which machine learning methods were used, and how were they selected?\",\"answer\":\"Random Forest and Gaussian Process Regression were trained using an optimized feature set; the model with better predictive power was chosen for further study.\"},{\"question\":\"How was the synergistic effect of two co-dopants assessed?\",\"answer\":\"The actual specific discharge capacities were compared with expected values derived from superimposing the machine learning predictions.\"}]","Machine learning approach to the possible synergy between co-doped elements in the case of LiFePO4/C | PDF",1785945709,53,{"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},"machine-learning-approach-to-the-possible-synergy-between-co-doped-elements-in-the-case-of-lifepo4c","",{"@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/machine-learning-approach-to-the-possible-synergy-between-co-doped-elements-in-the-case-of-lifepo4c/128215/",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},"How was the dataset for co-doping synergy modeling constructed?","Question",{"text":76,"@type":77},"A dataset was created from existing literature, containing information on doped LFP structures with a single element dopant before extending to co-doping analysis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods were used, and how were they selected?",{"text":81,"@type":77},"Random Forest and Gaussian Process Regression were trained using an optimized feature set; the model with better predictive power was chosen for further study.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the synergistic effect of two co-dopants assessed?",{"text":85,"@type":77},"The actual specific discharge capacities were compared with expected values derived from superimposing the machine learning predictions.","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"]