[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122733-en":3,"doc-seo-122733-105":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},122733,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Functional Material Systems Enabled by Automated Data Extraction and Machine Learning","Functional material systems rely on hierarchical organization of molecular building blocks to meet application-specific targets, yet the design space is large and traditional experimental screening is slow and expensive. Machine learning can predict chemical syntheses and material properties, but performance depends on high-quality training and validation data. Automated extraction of structured information from scientific literature supports building large datasets for ML. The work also considers how large language models can aid extraction, and why research data management tools are necessary to address limitations and ensure stable, synthesizable, scalable, and sustainable materials.","PERSPECTIVE  \n[www.afm-journal.de](www.afm-journal.de)  \nFunctional Material Systems Enabled by Automated Data Extraction and Machine Learning  \nPayam Kalhor, Nicole Jung, Stefan Bräse, Christof Wöll, Manuel Tsotsalas,* and Pascal Friederich*  \nThe development of new functional materials is crucial for addressing global challenges such as clean energy or the discovery of new drugs and antibiotics. Functional material systems are typically composed of functional molecular building blocks, organized across multiple length scales in a hierarchical order. The large design space allows for precise tuning of properties to specific applications, but also makes it time-consuming and expensive to screen for optimal structures using traditional experimental methods. Machine learning (ML) models can potentially revolutionize the field of materials science by predicting chemical syntheses and materials properties with high accuracy. However, ML models require data to be trained and validated. Methods to automatically extract data from scientific literature make it possible to build large and diverse datasets for ML models. In this article, opportunities and challenges of data extraction and machine learning methods are discussed to accelerate the discovery of high-performing functional material systems, while ensuring that the predicted materials are stable, synthesizable, scalable, and sustainable. The potential impact of large language models (LLMs) on the data extraction process are discussed. Additionally, the importance of research data management tools is discussed to overcome the intrinsic limitations of data extraction approaches.  \ndifferent disciplines to achieve optimal design. [1–3] This includes the components of materials, the structure of materials across different length scales, and every aspect of the final device and its operation conditions. [4–7] Additionally, environmental impact, circularity, and sustainability become increasingly important. All these individual aspects represent objectives for the design of functional material systems. To navigate this multidimensional design space with multiple objectives, researchers need to work across different disciplines in joint projects, considering expertise and research from these different disciplines.[8–10] To support and enable research in the area of functional material systems, automated data extraction from literature, using natural language processing, combined with ML can be used to operate on large amounts of data representing community knowledge to complement the researchers’ own knowledge and experimental results. [11–13] Thus, a collaborative and interdisciplinary approach, coupled with the use of automated data extraction and machine learning, is  \n1. Introduction  \nA current challenge for research on functional material systems is the need to simultaneously consider multiple aspects from  \nnecessary to enable the development of functional material systems that optimally meet multiple objectives. After identifying the optimal design of functional material systems, the synthesis of such complex hierarchically organized materials represents an  \nP. Kalhor, P. Friederich  \nInstitute of Nanotechnology Karlsruhe Institute of Technology  \nHermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  \n[E-mail: pascal.friederich@kit.edu](E-mail: pascal.friederich@kit.edu)  \nP. Kalhor, P. Friederich  \nInstitute of Theoretical Informatics  \nKarlsruhe Institute of Technology Am Fasanengarten 5, 76131 Karlsruhe, Germany  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/adfm.202302630](https://doi.org/10.1002/adfm.202302630)[ ](https://doi.org/10.1002/adfm.202302630)© 2023 The Authors. Advanced Functional Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided ","cbCaibS78juwjZi9","https://ap.wps.com/l/cbCaibS78juwjZi9","pdf",1394233,1,11,"English","en",105,"# Introduction\n## Data extraction from literature for ML\n## Opportunities and challenges for accelerating discovery\n## Role of large language models\n## Research data management tools","[{\"question\":\"Why are functional material system designs challenging to screen with traditional experiments?\",\"answer\":\"The design space is large and properties must be tuned across multiple length scales, making experimental screening time-consuming and expensive.\"},{\"question\":\"How does automated data extraction support machine learning in materials science?\",\"answer\":\"Automated extraction from scientific literature enables building large, diverse datasets that can be used to train and validate ML models for predicting syntheses and properties.\"},{\"question\":\"What additional considerations are discussed beyond prediction accuracy?\",\"answer\":\"The article emphasizes that predicted materials should be stable, synthesizable, scalable, and sustainable, and highlights constraints that extraction approaches face.\"}]","Functional Material Systems Enabled by Automated Data Extraction and Machine Learning | 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are functional material system designs challenging to screen with traditional experiments?","Question",{"text":75,"@type":76},"The design space is large and properties must be tuned across multiple length scales, making experimental screening time-consuming and expensive.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does automated data extraction support machine learning in materials science?",{"text":80,"@type":76},"Automated extraction from scientific literature enables building large, diverse datasets that can be used to train and validate ML models for predicting syntheses and properties.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional considerations are discussed beyond prediction accuracy?",{"text":84,"@type":76},"The article emphasizes that predicted materials should be stable, synthesizable, scalable, and sustainable, and highlights constraints that extraction approaches 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