[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127392-en":3,"doc-seo-127392-105":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},127392,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning assisted screening of metal binary alloys for anode materials - strategy and CGCNN prediction","Battery alloy anode materials are central to high-performance energy storage, yet traditional discovery and optimization relies on inefficient, time-consuming screening. This work presents a machine learning-assisted strategy that accelerates candidate identification by learning from a large dataset compiled from the MP and AFLOW databases. A CGCNN is used to predict formation-related properties including potential and specific capacity, then benchmarked against experimental data. The method highlights about 120 metal binary alloy anodes with low potential and high specific capacity across Li, Na, K, Zn, Mg, Ca, and Al-based battery systems.","Machine learning assisted screening of metal binary alloys for anode materials  \nXingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong  \nSchool of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China  \nAbstract  \nIn the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a machine learning-assisted strategy to expedite the discovery and optimization of these materials.  \nWe compiled a vast dataset from the MP andAFLOW databases, encompassing tens of thousands of alloy compositions and properties. Utilizing a CGCNN, we accurately  \npredicted the potential and specific capacity of alloy anodes, validated against experimental data. This approach identified approximately 120 low potential and high  \nspecific capacity alloy anodes suitable for various battery systems including Li, Na, K, Zn, Mg, Ca, and Al-based. Our method not only streamlines the screening of battery  \nanode materials but also propels the advancement of battery material research and innovation in energy storage technology.  \nKeywords: battery; alloy anode; CGCNN; potential; specific capacity; candidate  \nIntroduction  \nBattery is one of the most important energy storage technologies and plays a key role in new energy vehicles and electronic devices [1] . Anode materials are a critical component of batteries, directly impacting their energy density, cycle life, and safety [2] .  \nTraditional anode materials like graphite have a low theoretical capacity (372 mAh g- 1), which doesn't meet the demand for high energy density batteries [3] . Silicon and germanium anode materials have high theoretical specific capacity (Si: 4200 mAh g-1, Ge: 1600 mAh g-1) [4], but they suffer from serious capacity degradation and undergo large volume expansions during charging and discharging, leading to structural damage and electrode failure. Silicon-based anode materials can form an unstable solid electrolyte interphase (SEI) layer, reducing Coulombic Efficiency [5] . Metal anodes face challenges such as safety concerns due to dendrite growth and low Coulombic Efficiency from volume expansion. Lead anodes, due to their high density, are not suitable for high-energy-density batteries.  \nIn contrast, alloy anode materials have attracted much attention due to their higher energy density, specific capacity, and good rate capability [6] . Alloy materials can also effectively inhibit the growth of dendrites by lowering the embedding potential and optimizing the electrode structure, thus improving the safety and cycling stability of batteries. Therefore, the development of new high-capacity and high-stability anode materials is crucial for improving battery performance. Alloy-type anode materials, such as Sb, Bi, and Sn, show high theoretical capacities, but still face many challenges  \nin practical applications, and further in-depth research is needed to overcome these challenges.  \nIn recent years, data-driven machine learning techniques have emerged as one of the hotspots of research in the field of materials science and engineering. It can quickly identify material performance, predict material behavior and optimize material design by analyzing and processing large amounts of data, thus significantly shortening the research and development cycle of new materials and improving research and development efficiency. In the battery field, the application of machine learning is particularly important [7] . The development of battery materials is a complex and timeconsuming process, involving a combination of multiple chemical compositions and physical structures. The traditional trial-and-error method is not only time-consuming and labor-intensive, but also costly. Machine learning techniques can learn from massive amounts of experimental data, discover the correlation between","cbCaiqjjYtHPN6kE","https://ap.wps.com/l/cbCaiqjjYtHPN6kE","pdf",1926610,1,41,"English","en",105,"# Introduction\n## Limitations of traditional anode screening\n## Motivation for alloy anode materials\n## Role of machine learning in battery materials\n# Method and Screening Criteria\n## CGCNN-based screening workflow\n## Evaluation of formation energy, potential, and specific capacity\n# Results and Candidate Selection","[{\"question\":\"Why are traditional anode material screening methods considered inefficient?\",\"answer\":\"Traditional screening is time-consuming and labor-intensive, making the discovery and optimization of battery anode materials slow and costly.\"},{\"question\":\"What properties does the CGCNN model predict for alloy anode candidates?\",\"answer\":\"The approach uses CGCNN to obtain key electrochemical-relevant properties, including potential and specific capacity (along with formation-related information).\"},{\"question\":\"What types of batteries are addressed by the identified alloy anodes?\",\"answer\":\"The selected low-potential, high-specific-capacity alloy anodes are suitable for several battery systems, including Li, Na, K, Zn, Mg, Ca, and Al-based batteries.\"}]","Machine learning assisted screening of metal binary alloys for anode materials - strategy and CGCNN prediction | PDF",1785938651,103,{"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-assisted-screening-of-metal-binary-alloys-for-anode-materials-strategy-and-cgcnn-prediction","",{"@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-assisted-screening-of-metal-binary-alloys-for-anode-materials-strategy-and-cgcnn-prediction/127392/",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","2026-08-05",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 traditional anode material screening methods considered inefficient?","Question",{"text":75,"@type":76},"Traditional screening is time-consuming and labor-intensive, making the discovery and optimization of battery anode materials slow and costly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What properties does the CGCNN model predict for alloy anode candidates?",{"text":80,"@type":76},"The approach uses CGCNN to obtain key electrochemical-relevant properties, including potential and specific capacity (along with formation-related information).",{"name":82,"@type":73,"acceptedAnswer":83},"What types of batteries are addressed by the identified alloy anodes?",{"text":84,"@type":76},"The selected low-potential, high-specific-capacity alloy anodes are suitable for several battery systems, including Li, Na, K, Zn, Mg, Ca, and Al-based batteries.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]