[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127381-en":3,"doc-seo-127381-105":30,"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":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},127381,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Predicting Miscibility in Binary Compounds - A Machine Learning and Genetic Algorithm Study","Data science and materials informatics accelerate the synthesis of multi-component compounds. The study predicts miscibility in binary compounds from atomic-level data by combining Materials Project (MP) and ICSD experimental records, building a dataset covering 2,346 binary systems. A random-forest classifier learns and analyzes miscibility-relevant factors, then screens binaries with high synthetic potential. Using genetic algorithms on the Co–Eu system, the work identifies three new thermodynamically stable phases, providing guidance for experimental synthesis in binary and complex material systems.","arXiv :2409 .02633v1 [ cond-mat .mtrl-sci ] 4 Sep 2024  \nPredicting Miscibility in Binary Compounds: A Machine Learning and Genetic Algorithm Study  \nChiwen Feng,† Yanwei Liang,† Jiaying Sun,‡ Renhai Wang,∗ ,†,¶ Huaijun Sun,∗ , §  \nand Huafeng Dong†,¶  \n†School of Physics and Optoelectronic Engineering, Guangdong University of Technology,  \nGuangzhou 510006, China  \n‡College of Chemistry, Zhengzhou University, Zhengzhou 450001, China ¶Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangdong University of Technology, Guangzhou 510006, China  \n§Jiyang College of Zhejiang Agriculture and Forestry University, Zhuji, 311800, China  \nE-mail: [wangrh@gdut.edu.cn](wangrh@gdut.edu.cn) ; [hjsun@zafu.edu.cn](hjsun@zafu.edu.cn)  \nAbstract  \nThe combination of data science and materials informatics has significantly propelled the advancement of multi-component compound synthesis research. This study employs atomic-level data to predict miscibility in binary compounds using machine learning, demonstrating the feasibility of such predictions. We have integrated experimental data from the Materials Project (MP) database and the Inorganic Crystal Structure Database (ICSD), covering 2,346 binary systems. We applied a random forest classification model to train the constructed dataset and analyze the key factors affecting the miscibility of binary systems and their significance while predicting binary systems with high synthetic potential. By employing advanced genetic algorithms on the Co-Eu system, we discovered three novel thermodynamically stable phases, CoEu8 ,  \nFigure 1: Four paradigms of science: empirical, theoretical, computational and data-driven  \nCo3 Eu2 , and CoEu. This research offers valuable theoretical insights to guide experimental synthesis endeavors in binary and complex material systems.  \nIntroduction  \nThe field of materials science has consistently been a focal point for scientific research and innovation, leading to numerous in-depth and extensive R&D activities. The evolution of materials science research has progressed through four major paradigms: experimental science, theoretical laws, computational modeling, and data-driven approaches (Figure 1) . 1,2 The initial paradigm relied on experimental trial and error, a process characterized by long development cycles, potentially spanning 10-20 years and high experimental costs.  \nOnly a few centuries ago, materials science began to transition from trial-and-error methods to a more systematic and theoretical approach, spurred by the development of physical theoretical models and general laws such as thermodynamic constants. This shift marked anew focus on “material design”. However, as calculations became more complex, theoretical computing in its second paradigm encountered significant bottlenecks in simulating intricate phenomena. It was not until the advancement of computers over the past decade that the third paradigm emerged, facilitating virtual laboratory simulations of real-world phenomena and enabling the “synthesis” of new materials.  \n1 C.F. and Y.L. contributed equally to this paper  \nFirst-principles calculations based on Density Functional Theory (DFT), 3 Local Density Approximation (LDA), and Generalized Gradient Approximation (GGA) 4 have been widely used to study the properties of new binary materials. 5,6 Meanwhile, significant advancements in ground-state structure prediction tools have completely transformed the field of materials science, making it possible to predict new material structures before experimental synthesis. For example, methods such as Genetic Algorithms/Evolutionary Algorithms, 7,8 Particle Swarm Optimization, 9,10 Random Sampling, 11,12 Minima Hopping, 13,14 Simulated Annealing, 15 Topological Modeling Method, 16 and Firefly Algorithm 17,18 have achieved notable success. Among them, is the Universal Structure Predictor: Evolutionary Xtallography (USPEX), known for its powerful search capab","cbCaiuyHabFehshm","https://ap.wps.com/l/cbCaiuyHabFehshm","pdf",3419195,1,23,"English","en",105,"# Abstract\n# Introduction\n## Four paradigms in materials science\n## From experiment to theory to computation\n## Data-driven approaches and virtual screening\n## Materials databases supporting machine learning","[{\"question\":\"How is miscibility in binary compounds predicted in this study?\",\"answer\":\"The approach constructs an atomic-level dataset by integrating MP and ICSD data, then trains a random forest classifier to predict miscibility-related behavior and screen promising binaries.\"},{\"question\":\"What data sources and how many systems are used?\",\"answer\":\"The study integrates experimental data from the Materials Project (MP) and the Inorganic Crystal Structure Database (ICSD), covering 2,346 binary systems.\"},{\"question\":\"What role do genetic algorithms play, and what was found for the Co–Eu system?\",\"answer\":\"Genetic algorithms are applied to the Co–Eu system to discover three new thermodynamically stable phases: CoEu8, Co3Eu2, and CoEu.\"}]","Predicting Miscibility in Binary Compounds - A Machine Learning and Genetic Algorithm Study | PDF",1785938596,58,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-miscibility-in-binary-compounds-a-machine-learning-and-genetic-algorithm-study","",{"@graph":36,"@context":86},[37,54,69],{"@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/predicting-miscibility-in-binary-compounds-a-machine-learning-and-genetic-algorithm-study/127381/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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 is miscibility in binary compounds predicted in this study?","Question",{"text":76,"@type":77},"The approach constructs an atomic-level dataset by integrating MP and ICSD data, then trains a random forest classifier to predict miscibility-related behavior and screen promising binaries.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources and how many systems are used?",{"text":81,"@type":77},"The study integrates experimental data from the Materials Project (MP) and the Inorganic Crystal Structure Database (ICSD), covering 2,346 binary systems.",{"name":83,"@type":74,"acceptedAnswer":84},"What role do genetic algorithms play, and what was found for the Co–Eu system?",{"text":85,"@type":77},"Genetic algorithms are applied to the Co–Eu system to discover three new thermodynamically stable phases: CoEu8, Co3Eu2, and CoEu.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]