[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126701-en":3,"doc-seo-126701-105":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},126701,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels","Large chemical spaces make the design of functional, responsive soft materials challenging, but also enable diverse property opportunities. The study reports an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries. Analysis data from photodegradation of over 900 hydrogel pad types train a machine-learning model for automated decision making. Iterative Bayesian optimization substantially improves response properties, widening accessible material property space while reducing cost and time.","RESEARCH ARTICLE  \n[www.small-methods.com](www.small-methods.com)  \nHigh-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels  \nMaximilian Seifermann, Patrick Reiser, Pascal Friederich,* and Pavel A. Levkin*  \nDue to the large chemical space, the design of functional and responsive soft materials poses many challenges but also offers a wide range of opportunities in terms of the scope of possible properties. Herein, an experimental workflow for miniaturized combinatorial high-throughput screening of functional hydrogel libraries is reported. The data created from the analysis of the photodegradation process of more than 900 different types of hydrogel pads are used to train a machine learning model for automated decision making. Through iterative model optimization based on Bayesian optimization, a substantial improvement in response properties is achieved and thus expanded the scope of material properties obtainable within the chemical space of hydrogels in the study. It is therefore demonstrated that the potential of combining miniaturized high-throughput experiments with smart optimization algorithms for cost and time efficient optimization of materials properties.  \nThereis a necessity for an ongoing signalresponse relation between a material and its environment when full control ofcomplex processes such as the behavior of cells/tissue is required, the higher the amount of control the more complex the relation needs to be.[1] While advantagesin material design are rapid, systems responding to various stimuli at once or ina complex manner are scarce and usually consist of a combination of different materials.[2] This multimaterial approach requires a large amount of materials to choose from in order to tailor the exact properties needed. While theoretically possible, producing the required amount of data in traditional fashion isnot feasible, not least due to the enormous consumption of materials required  \n1. Introduction  \nOne of the key features material science is often trying to mimic is the ability of biological systems to respond to external stimuli.  \nM. Seifermann, P. A. Levkin  \nInstitute of Biological and Chemical Systems-Functional Molecular Systems  \nKarlsruhe Institute of Technology  \nHermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany  \n[E-mail: levkin@kit.edu](E-mail: levkin@kit.edu)  \nP. Reiser, 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. Reiser, P. Friederich  \nInstitute of Theoretical Informatics  \nKarlsruhe Institute of Technology Am Fasanengarten 5, 76131 Karlsruhe, Germany  \nP. A. Levkin  \nInstitute of Organic Chemistry Karlsruhe Institute of Technology Fritz-Haber-Weg 6, Karlsruhe, Germany  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/smtd.202300553](https://doi.org/10.1002/smtd.202300553)[ ](https://doi.org/10.1002/smtd.202300553)© 2023 The Authors. Small Methods 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 the original work is properly cited.  \nDOI: 10.1002/smtd.202300553  \nfor this as well as the processing of the sheer amount of data produced from it.  \nThroughout the last decade, machine learning (ML) methods[3] have proven useful in many research areas such as material science,[4–7] chemistry,[8–11] biology,[12–15] and drug discovery.[16–19] One major advantage of machine learning methods is the utilization of the vast amount of data generated in decades of research to achieve faster progress in science, e.g. by improving the virtual design of materials,[20] speeding up optimization processes[21–23] or yielding a better understanding of hidden relations","cbCaioODLjL3RcFr","https://ap.wps.com/l/cbCaioODLjL3RcFr","pdf",3091708,1,11,"English","en",105,"# Introduction\n## Signal–response complexity in material design\n## Data generation limits and the need for throughput\n## Role of machine learning in materials and science\n## Miniaturization and automated high-throughput platforms\n## From basic readouts to advanced HT screening","[{\"question\":\"Why is high-throughput experimentation important for designing photodegradable hydrogels?\",\"answer\":\"Traditional approaches cannot generate enough data within reasonable time and resource limits for training and optimizing machine-learning models. High-throughput and miniaturization increase data generation efficiency.\"},{\"question\":\"How is machine learning used in the hydrogel design workflow?\",\"answer\":\"The workflow uses analysis data from photodegradation experiments of more than 900 hydrogel types to train a machine-learning model that supports automated decision making during design.\"},{\"question\":\"What optimization strategy improves hydrogel response properties?\",\"answer\":\"Iterative model optimization based on Bayesian optimization leads to substantial improvements in the hydrogel response properties, expanding the range of obtainable material characteristics.\"}]","High-Throughput Synthesis and Machine Learning Assisted Design of Photodegradable Hydrogels | PDF",1785934306,28,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"high-throughput-synthesis-and-machine-learning-assisted-design-of-photodegradable-hydrogels","",{"@graph":36,"@context":85},[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/high-throughput-synthesis-and-machine-learning-assisted-design-of-photodegradable-hydrogels/126701/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is high-throughput experimentation important for designing photodegradable hydrogels?","Question",{"text":75,"@type":76},"Traditional approaches cannot generate enough data within reasonable time and resource limits for training and optimizing machine-learning models. High-throughput and miniaturization increase data generation efficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning used in the hydrogel design workflow?",{"text":80,"@type":76},"The workflow uses analysis data from photodegradation experiments of more than 900 hydrogel types to train a machine-learning model that supports automated decision making during design.",{"name":82,"@type":73,"acceptedAnswer":83},"What optimization strategy improves hydrogel response properties?",{"text":84,"@type":76},"Iterative model optimization based on Bayesian optimization leads to substantial improvements in the hydrogel response properties, expanding the range of obtainable material characteristics.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"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":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]