[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128751-en":3,"doc-seo-128751-105":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},128751,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Study of Cathode Materials for Na-Ion Batteries - Comparison Between Machine Learning Predictions and Density Functional Theory Calculations","Research on energy storage focuses on replacing lithium-ion batteries due to supply-chain constraints, cost, and safety risks, motivating increasing attention to sodium-ion systems. This study investigates diverse Na-ion cathode compositions using first-principles workflows built on the AiiDA framework. Crystal graph convolutional neural networks and geometric crystal graph neural networks are trained to forecast formation energy and validated against density functional theory calculations, enabling faster, disruptive materials discovery than conventional physics-based simulations.","Article  \nStudy of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations  \nClaudio Ronchetti 1, Sara Marchio 2, Francesco Buonocore 2, *, Simone Giusepponi 2, Sergio Ferlito 3 and Massimo Celino 2  \nCitation: Ronchetti, C.; Marchio, S.; Buonocore, F.; Giusepponi, S.; Ferlito, S.; Celino, M. Study of Cathode Materials for Na-Ion Batteries:  \nComparison Between Machine Learning Predictions and Density Functional Theory Calculations. Batteries 2024, 10, 431. [https://](https://)[ ](https://)[doi.org/10.3390/batteries10120431](doi.org/10.3390/batteries10120431)  \nAcademic Editor: Shaokun Chong  \nReceived: 30 September 2024  \nRevised: 27 November 2024  \nAccepted: 3 December 2024  \nPublished: 5 December 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Telespazio S.p.A., Via Tiburtina 965, 00156 Rome, Italy; [claudio.ronchetti@telespazio.com](claudio.ronchetti@telespazio.com)  \n2 Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—  \nC. R. Casaccia, Via Anguillarese 301, 00123 Rome, Italy; [sara.marchio@enea.it](sara.marchio@enea.it) (S.M.); [simone.giusepponi@enea.it](simone.giusepponi@enea.it) (S.G.); [massimo.celino@enea.it](massimo.celino@enea.it) (M.C.)  \n3 Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—  \nC. R. Portici, Piazzale Enrico Fermi 1, 80055 Portici, Italy; sergio.ferlito@enea.it  \n* Correspondence: [francesco.buonocore@enea.it](francesco.buonocore@enea.it)  \nAbstract: Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods.  \nKeywords: DFT calculations; neural networks; machine learning; electrochemical energy storage; Na-ion; high-throughput calculations  \n1. Introduction  \nAs global technology advances, humanity faces significant challenges in terms of pollution and climate impact. The current trajectory is unsustainable; while technological progress improves our quality of life, it simultaneously endangers the planet. A critical shift is necessary, from relying on fossil fuels like oil and coal for energy production to adopting renewable energy sources [1] . Renewable energy primarily involves converting power from the Sun, wind, and oceans into electricity. However, these energy sources do not incessantly produce power, creating a need for effective energy storage solutions.  \nLithium-ion batteries (LIBs) have been highly successful in meeting energy storage demands in recent years [2] . Yet, with the growing energy needs, particularly in sectors like transportation where the shift from fossil fuels to electricity is acceleratin","cbCaicEVHl1IOhM9","https://ap.wps.com/l/cbCaicEVHl1IOhM9","pdf",1693779,4,1,12,"English","en",105,"# Introduction\n## Energy storage needs and motivation for Na-ion batteries\n## Limitations of lithium-ion batteries\n## Sodium-ion batteries as an alternative\n# Materials and Methods\n## Cathode materials and structural phases\n## Computational workflow using AiiDA\n## Machine learning models and training targets\n# Results and Discussion\n## Prediction of formation energy\n## Comparison with density functional theory calculations\n## Throughput and performance considerations\n# Conclusions\n## Key findings and implications for materials discovery","[{\"question\":\"Why are sodium-ion batteries studied as an alternative to lithium-ion batteries?\",\"answer\":\"Lithium-ion systems face cost, supply, and safety issues, including concerns related to lithium reactivity. Sodium is more abundant and less reactive, making sodium-ion batteries a promising and safer alternative for energy storage.\"},{\"question\":\"What computational approach is used to study Na-ion cathode materials in this work?\",\"answer\":\"The study uses first-principles calculations organized as workflow chains within the AiiDA framework to explore a broad range of cathode material compositions.\"},{\"question\":\"How does the machine learning component relate to density functional theory calculations?\",\"answer\":\"Crystal graph neural networks are trained to predict the formation energy of candidate materials, and their predictions are demonstrated to match formation energies computed via density functional theory.\"}]","Study of Cathode Materials for Na-Ion Batteries - Comparison Between Machine Learning Predictions and Density Functional Theory Calculations | PDF",1786003099,30,{"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},"study-of-cathode-materials-for-na-ion-batteries-comparison-between-machine-learning-predictions-and-density-functional-theory-calculations","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"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":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/study-of-cathode-materials-for-na-ion-batteries-comparison-between-machine-learning-predictions-and-density-functional-theory-calculations/128751/",{"url":53,"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","2026-08-25","2026-08-06",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 are sodium-ion batteries studied as an alternative to lithium-ion batteries?","Question",{"text":76,"@type":77},"Lithium-ion systems face cost, supply, and safety issues, including concerns related to lithium reactivity. Sodium is more abundant and less reactive, making sodium-ion batteries a promising and safer alternative for energy storage.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What computational approach is used to study Na-ion cathode materials in this work?",{"text":81,"@type":77},"The study uses first-principles calculations organized as workflow chains within the AiiDA framework to explore a broad range of cathode material compositions.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the machine learning component relate to density functional theory calculations?",{"text":85,"@type":77},"Crystal graph neural networks are trained to predict the formation energy of candidate materials, and their predictions are demonstrated to match formation energies computed via density functional theory.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"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":20,"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":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]