[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85724-en":3,"doc-seo-85724-105":29,"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":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":13,"seo_description":14,"update_tm":27,"read_time":28},85724,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Autonomous Scientific Knowledge Generation Framework for AI-Driven Scientific Discovery","Artificial intelligence is rapidly transforming scientific discovery but remains limited by the availability of high-quality, structured scientific knowledge. Much essential information for predictive modeling and inverse design is embedded in unstructured literature such as narratives, tables, figures, equations, and supplementary materials. A new Autonomous Scientific Knowledge Generation Framework systematically converts scientific literature into a Unified AI-Ready Scientific Knowledge Base. It combines ontology-guided acquisition, hybrid extraction, semantic harmonization, evidence-based fusion, and ontology validation within a unified architecture.","An Autonomous Scientific Knowledge Generation Framework for  \nAI-Driven Scientific Discovery  \nDibakar Datta  \nDepartment of Mechanical and Industrial Engineering New jersey Institute of Technology (NJIT), Newark, NJ 07052, USA Email: [ddlab@njit.edu](ddlab@njit.edu) ; Phone: +1 973-596-3647  \nAbstract  \nArtificial intelligence (AI) is rapidly transforming scientific discovery. However, its effectiveness remains fundamentally constrained by the availability of high-quality, structured scientific knowledge (data) . Although community databases have accelerated data-driven materials research, much of the scientific knowledge required for predictive modeling and inverse materials design remains embedded within unstructured literature, including narrative text, tables, figures, equations, and supplementary materials. Transforming this heterogeneous information into machine-interpretable knowledge remains a major challenge for next-generation AI-driven scientific discovery. Here, we present an Autonomous Scientific Knowledge Generation Framework that systematically transforms scientific literature into a Unified AIReady Scientific Knowledge Base. The framework integrates ontology-guided literature acquisition, hybrid scientific knowledge extraction, semantic harmonization, evidence-based knowledge fusion, and ontologyguided validation within a unified computational architecture. Rather than treating literature retrieval, information extraction, and database construction as independent tasks, the proposed framework progressively converts distributed scientific observations into structured, semantically consistent, provenance-preserving knowledge suitable for downstream AI-driven reasoning. The framework is demonstrated using electro-optic materials as a representative benchmark because of their complex tensor properties, multidimensional operating conditions, and heterogeneous reporting conventions. Autonomous literature acquisition retrieved and validated approximately 1,000 ‘best’ domain-specific publications from multiple scholarly repositories. A representative subset of eight publications was subsequently processed through the complete workflow, generating 29 structured scientific records, which were harmonized into 7 canonical scientific records. The demonstration illustrates the complete transformation from scientific publications to structured knowledge and ultimately to an AI-ready knowledge base while preserving quantitative measurements together with their associated materials, operating conditions, provenance, and scientific context. Beyond literature mining, the proposed framework establishes a general architecture for autonomous scientific knowledge generation. By integrating literature-derived knowledge with computational simulations, experimental measurements, existing scientific repositories, and future observations, the framework provides the information infrastructure required for predictive scientific intelligence, generative scientific intelligence, and closed-loop autonomous scientific discovery. Because the underlying architecture is domain agnostic, it can be readily adapted to batteries, quantum materials, catalysis, semiconductors, polymers, biomaterials, nanomedicine, and other data-intensive scientific disciplines, providing a scalable foundation for next-generation AI-assisted scientific discovery.  \nKey Words: Autonomous Scientific Knowledge Generation, AI-Ready Scientific Knowledge Base, Scientific Knowledge Extraction, Artificial Intelligence for Scientific Discovery, Materials Informatics  \n1. INTRODUCTION  \nArtificial intelligence (AI) is transforming the way scientific knowledge is generated, analyzed, and utilized 1, 2 . Advances in machine learning (ML)3, natural language processing (NLP)4, deep learning5, large language models (LLMs)6, and generative AI7 have enabled computational systems to recognize complex patterns, integrate heterogeneous information, perform scientific reasoning, and assist","cbCaidaHgC1axZBO","https://ap.wps.com/l/cbCaidaHgC1axZBO","pdf",4635136,1,32,"English","en",105,"# Abstract\n## Introduction\n## Knowledge Generation Framework\n## Demonstration and Benchmark Results\n## Generalized Architecture and Applications","[{\"question\":\"Why is structured scientific knowledge critical for AI-driven discovery?\",\"answer\":\"AI model performance is constrained by the quality, completeness, and consistency of the scientific knowledge used for training. Even advanced algorithms learn only from the data provided.\"},{\"question\":\"What challenge does the framework target regarding scientific literature?\",\"answer\":\"Key scientific information is often trapped in unstructured sources like narrative text, tables, figures, equations, and supplementary materials. The framework addresses the need to transform this heterogeneous information into machine-interpretable knowledge.\"},{\"question\":\"How is the framework validated in the provided demonstration?\",\"answer\":\"It autonomously acquired and validated about 1,000 best domain-specific publications from multiple repositories, then processed eight publications through the full workflow to produce structured records that were harmonized into canonical records while preserving measurements, conditions, provenance, and context.\"}]",1784205822,81,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"autonomous-scientific-knowledge-generation-framework-for-ai-driven-scientific-discovery","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/autonomous-scientific-knowledge-generation-framework-for-ai-driven-scientific-discovery/85724/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is structured scientific knowledge critical for AI-driven discovery?","Question",{"text":75,"@type":76},"AI model performance is constrained by the quality, completeness, and consistency of the scientific knowledge used for training. Even advanced algorithms learn only from the data provided.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge does the framework target regarding scientific literature?",{"text":80,"@type":76},"Key scientific information is often trapped in unstructured sources like narrative text, tables, figures, equations, and supplementary materials. The framework addresses the need to transform this heterogeneous information into machine-interpretable knowledge.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the framework validated in the provided demonstration?",{"text":84,"@type":76},"It autonomously acquired and validated about 1,000 best domain-specific publications from multiple repositories, then processed eight publications through the full workflow to produce structured records that were harmonized into canonical records while preserving measurements, conditions, provenance, and context.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"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":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]