[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125612-en":3,"doc-seo-125612-105":30,"detail-sidebar-cat-0-en-105":83},{"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},125612,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries - abstract","Data-driven methods are increasingly used to estimate material properties, but high-precision machine learning typically needs large training sets generated at substantial computational cost. This work proposes an SVR-based model to predict the stability of silicon (Si)–alkaline metal alloys, emphasizing transferability to new Si alloys with different electronic configurations and structures. Using limited training data (~750 Si alloys) from the Material Project database, it evaluates XRD, SCM, and OFM descriptors and applies Grid Search CV and Bayesian Optimization to optimize energy-, formation-energy-, and packing-fraction predictions.","Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent  \nCation Batteries  \nJoy Datta, Dibakar Datta, Vidushi Sharma *  \nDepartment of Mechanical and Industrial Engineering, New Jersey Institute of Technology,  \nNewark, New Jersey 07103, United States  \n*Corresponding author: Vidushi Sharma, [Email: ](Email: vs574@njit.edu)[vs574@njit.edu](Email: vs574@njit.edu), [vidushis@ibm.com](vidushis@ibm.com)  \nAbstract  \nData-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si)– alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely XRay Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy  \nand packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced.  \nKeywords: Machine Learning, Batteries, Structural Descriptors, Support Vector Regression, Bayesian Optimization, Alloys, Silicon Anode  \n1. Introduction  \nIncreasing demand for electric vehicles (EVs) has highlighted the energy storage limitations of commercial graphite anode-based lithium-ion batteries (LIBs). Energy storage in graphite with an intercalation mechanism offers low gravimetric energy densities of 372 mAhg!\"1. Alternatively, energy storage in electrodes through a conversion mechanism can promise a tenfold improvement in energy densities. The most popular anode after graphite is Silicon (Si), which has a gravimetric energy density of 3572 mAhg!\" 2. Si reacts with incoming Li to form an alloying mixture of Li\\# Si during battery charging 3. Furthermore, there is a pressing issue surrounding the scarcity of Li for LIBs. To meet the requirements of the future EV industry, we cannot rely solely on non-renewable Li4. Active research efforts are being made to develop advanced battery technologies beyond Li ion5. Due to the high capacity offered by Si-Li anode, Si has found applications in alkali earth metal batteries such as sodium (Na) ion batteries6, magnesium (Mg) ion batteries7, and  \ncalcium(Ca) ion batteries8 , to name a few. Similar to Si-Li system, Si anode reacts with Mg to form Mg$ Si phase with a gravimetric density of 3816 mAhg!\" 9, and reacts with Ca to form Ca$ Si alloys with a maximum theoretical capacity of 3818 mAhg!\" 8. However, Si anode face challenges related to structural stability and volume expansion (~ 300% for LIBs) that lead to premature fractures, capacity losses, and limited cycle life of batteries 10–12. Therefore, before designing and experimenting such battery materials, ","cbCaiu54ecPadC4J","https://ap.wps.com/l/cbCaiu54ecPadC4J","pdf",2280908,1,34,"English","en",105,"# 1. Introduction\n## Background and motivation\n## Computational stability assessment with DFT\n# 2. Methods\n## SVR model and transferability focus\n## Structural descriptors (XRD, SCM, OFM)\n## Hyperparameter tuning (Grid Search CV, Bayesian Optimization)\n# 3. Results and Discussion\n## Descriptor performance for energy prediction\n## Transferability across multivalent systems\n# 4. Conclusions","[{\"question\":\"Which structural descriptor performs most reliably for predicting total energy of new Si alloys?\",\"answer\":\"XRD, an experimentally derived characterization, shows the most reliable performance for total energy prediction of new Si alloys.\"}]","Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries - abstract | PDF",1785900211,86,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"transferable-and-robust-machine-learning-model-for-predicting-stability-of-si-anodes-for-multivalent-cation-batteries-abstract","",{"@graph":36,"@context":77},[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/transferable-and-robust-machine-learning-model-for-predicting-stability-of-si-anodes-for-multivalent-cation-batteries-abstract/125612/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Which structural descriptor performs most reliably for predicting total energy of new Si alloys?","Question",{"text":75,"@type":76},"XRD, an experimentally derived characterization, shows the most reliable performance for total energy prediction of new Si alloys.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]