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The study presents a framework that utilizes BO for efficient and accurate prediction of material properties, focusing on the relationship between Ru flux and material characteristics such as RRR (Residual Resistivity Ratio) and EI (Electronic Interference). The methodology involves training machine learning models with varying sample sizes, ranging from 5 to 11 samples, to demonstrate the adaptability and effectiveness of BO across different data scales. The results, illustrated through plots of RRR and EI versus Ru flux, show that BO-driven predictions closely match experimental data, with confidence intervals indicating the model's uncertainty. The paper also visually represents the algorithms used in conjunction with training data, highlighting techniques like Bayesian optimization, tree-based algorithms, artificial neural networks, and support vector machines. Scatter plots are included to further validate the predictive capabilities of the models by comparing predicted versus actual values. The study emphasizes the potential of active learning and BO to accelerate the discovery and optimization of materials by intelligently guiding experimental design and reducing the number of required trials, thereby offering a more data-efficient approach to materials science research.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/bayesian-optimization-for-materials-discovery-from-high-throughput-screening-to-active-learning/194564/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/bayesian-optimization-for-materials-discovery-from-high-throughput-screening-to-active-learning/194564.png","ImageObject",442,249,{"name":88,"@type":89},"Theodora","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-02","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the primary focus of this research?","Question",{"text":108,"@type":109},"The primary focus is on applying Bayesian Optimization (BO) to accelerate materials discovery, moving beyond traditional high-throughput screening methods.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What material properties are being investigated in relation to Ru flux?",{"text":113,"@type":109},"The research investigates the relationship between Ru flux and material properties such as Residual Resistivity Ratio (RRR) and Electronic Interference (EI).",{"name":115,"@type":106,"acceptedAnswer":116},"What machine learning techniques are employed in this study?",{"text":117,"@type":109},"The study utilizes various machine learning techniques including Bayesian Optimization, tree-based algorithms, artificial neural networks, and support vector machines.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},194564,1788440317,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":25},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","| Parameters | Lower Limit Upper Limit | Instrument Precision | Number possible values |\n| --- | --- | --- | --- |\n| CW-laser power (W) | 0.01 5.55 | 0.01 | 554 |\n| Irradiation time (s) | 0.500 20.000 | 0.001 | 195 000 |\n| Gas pressure (kPa) | 0 6894.76 | 68.9476 | 100 |\n| Gas type | Argon Nitrogen Air | - | 3 |","cbCaidTYrrZT3amo","https://ap.wps.com/l/cbCaidTYrrZT3amo","pdf",9190546,43,"English","# Bayesian Optimization for Materials Discovery\n## From High-Throughput Screening to Active Learning\n## Methodology\n## Results","[{\"question\":\"What is the primary focus of this research?\",\"answer\":\"The primary focus is on applying Bayesian Optimization (BO) to accelerate materials discovery, moving beyond traditional high-throughput screening methods.\"},{\"question\":\"What material properties are being investigated in relation to Ru flux?\",\"answer\":\"The research investigates the relationship between Ru flux and material properties such as Residual Resistivity Ratio (RRR) and Electronic Interference (EI).\"},{\"question\":\"What machine learning techniques are employed in this study?\",\"answer\":\"The study utilizes various machine learning techniques including Bayesian Optimization, tree-based algorithms, artificial neural networks, and support vector machines.\"}]","Bayesian Optimization for Materials Discovery – From High-Throughput Screening to Active Learning | PDF"]