[{"data":1,"prerenderedAt":554},["ShallowReactive",2],{"nova_settings":3,"news-computer-vision-for-high-throughput-materials-synthesis-a-tutorial-for-experimentalists":43,"nova_blocks_header":134,"nova_blocks_footer":451,"related-news-computer-vision-for-high-throughput-materials-synthesis-a-tutorial-for-experimentalists":489,"i-material-symbols:menu":544,"i-material-symbols:keyboard-arrow-down":548,"i-material-symbols:person":550,"i-material-symbols:search":552},{"id":4,"user_created":5,"date_created":6,"user_updated":7,"date_updated":8,"example_solution_flex_light":9,"example_solution_flex_dark":12,"example_solution_swing_light":15,"example_solution_swing_dark":18,"example_solution_flexshuttle_light":21,"example_solution_flexshuttle_dark":24,"flex_module_light":27,"flex_module_dark":28,"tools_light":29,"tools_dark":32,"news":33,"news_single_page":36,"events":39,"events_single_page":42},1,"d42c9391-fa2d-4f14-ac19-e7b5ab24bd3a","2025-09-18T11:54:04.000Z","fa41fac3-5e7d-4734-9be7-2367112eb875","2025-09-29T07:46:28.000Z",{"id":10,"title":11},"a37fca0f-19b9-4548-b1ed-be66eaa7ff31","Example Solutions Light Configuration - Laboratory Automation Setup and Applications",{"id":13,"title":14},"67c85d9d-0c1e-4912-a8e0-cf2b05ec431a","Example Solutions Overview - Chemspeed Laboratory Automation Applications and Configurations",{"id":16,"title":17},"542f7e9e-4a3f-40ed-9c30-91bcb5ad6db8","SWING Example Solution Light Configuration - Automated Synthesis Platform Setup",{"id":19,"title":20},"c8d0175f-695a-4aa1-8d5e-277c53aa0778","SWING Example Solution Dark Configuration - Advanced Automated Synthesis Platform Setup",{"id":22,"title":23},"26fb7e48-4eb1-4e62-b3cd-088386845a96","FLEXSHUTTLE Example Solution Light Setup - Automated Sample Transport System for Laboratory",{"id":25,"title":26},"65202007-6f2b-434b-b293-de99f70c7816","FLEXSHUTTLE Example Solution Dark Configuration - Automated Sample Transport Platform",{"id":10,"title":11},{"id":13,"title":14},{"id":30,"title":31},"5ffdb292-ec47-4774-a413-76cb06835b34","Chemspeed Default Logo Blue Bg",{"id":30,"title":31},{"id":34,"title":35},"696fd543-bfdb-45e2-bcdd-8f3a47caf257","News Picture 1 1 V2",{"id":37,"title":38},"22d85ab9-6f8d-4238-adc6-acaf96c60e93","News Picture Generic",{"id":40,"title":41},"f27df6d7-2773-4f78-861c-317c4ecd3bac","Chemspeed Event's",{"id":40,"title":41},{"id":44,"featured_news":45,"news_type":46,"title":47,"url":48,"content":49,"content_editor":50,"target_date":111,"seo":112,"picture":49,"industry":115,"workflow":122},14002,false,"customer_news","Computer Vision for High-Throughput Materials Synthesis: A Tutorial for Experimentalists","computer-vision-for-high-throughput-materials-synthesis-a-tutorial-for-experimentalists",null,{"time":51,"blocks":52,"version":110},1762169386730,[53,61,67,73,78,84,90,96,102],{"id":54,"type":55,"data":56,"tunes":58},"KHd6OdI0UW","paragraph",{"text":57},"ChemRxiv",{"alignment":59},{"alignment":60},"left",{"id":62,"type":55,"data":63,"tunes":65},"Fe0nwJUDjU",{"text":64},"Advances in high-throughput instrumentation and laboratory automation are revolutionizing materials synthesis by enabling the rapid generation of large libraries of novel materials. However, efficient characterization of these synthetic libraries remains a significant bottleneck in the discovery of new materials. Traditional characterization methods are often limited to sequential analysis, making them time-intensive and cost-prohibitive when applied to large sample sets. In the same way that chemists interpret visual indicators to identify promising samples, computer vision (CV) is an efficient approach to accelerate materials characterization across varying scales when visual cues are present. CV is particularly useful in high-throughput synthesis and characterization workflows, as these techniques can be rapid, scalable, and cost-effective. Although there is a set of growing examples in the literature, we have found a lack of resources where newcomers interested in the field could get a hold of a practical way to get started. Here, we aim to fill that identified gap and present a structured tutorial for experimentalists to integrate computer vision into high-throughput materials research, providing a detailed roadmap from data collection to model validation. Specifically, we describe the hardware and software stack required for deploying CV in materials characterization, including image acquisition, annotation strategies, model training, and performance evaluation. As a case study, we demonstrate the implementation of a CV workflow within a high-throughput materials synthesis and characterization platform to investigate the crystallization of metal–organic frameworks (MOFs). By outlining key challenges and best practices, this tutorial aims to equip chemists and materials scientists with the necessary tools to harness CV for accelerating materials discovery.",{"alignment":66},{"alignment":60},{"id":68,"type":55,"data":69,"tunes":71},"Mhpo67aePV",{"text":70},"For details: ",{"alignment":72},{"alignment":60},{"id":74,"type":55,"data":75,"tunes":76},"w4_eRMh7sS",{"text":47},{"alignment":77},{"alignment":60},{"id":79,"type":55,"data":80,"tunes":82},"53u05GasbF",{"text":81},"Madeleine A. Gaidimas a,†, Abhijoy Mandal b,†, Pan Chen b, Shi Xuan Leong c,d, Gyu-Hee Kim,a Akshay Talekar e, Kent O. Kirlikovali a, Kourosh Darvish b,f, Omar K. Farha a,g, Varinia Bernales c,e,f, Alán Aspuru-Guzik b,c,f,h,i,j,k,l",{"alignment":83},{"alignment":60},{"id":85,"type":55,"data":86,"tunes":88},"x8XxWbJf0S",{"text":87},"a) Department of Chemistry and International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, United States\u003Cbr>b) Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada\u003Cbr>c) Department of Chemistry, University of Toronto, Toronto, ON M5S 2E4, Canada\u003Cbr>d) School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371\u003Cbr>e) Materials Discovery Research Institute, UL Research Institutes, Skokie, IL 60077, United States\u003Cbr>f) Acceleration Consortium, Toronto, ON M5S 3H6, Canada\u003Cbr>g) Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, United States\u003Cbr>h) Vector Institute for Artificial Intelligence, Toronto, ON M5G 1M1, Canada\u003Cbr>i) Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada\u003Cbr>j) Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada\u003Cbr>k) Senior Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5G 1M1, Canada\u003Cbr>l) NVIDIA, Toronto, ON M5V 1K4, Canada\u003Cbr>† Equal contribution",{"alignment":89},{"alignment":60},{"id":91,"type":55,"data":92,"tunes":94},"nwlP10T90V",{"text":93},"ChemRxiv\u003Cbr>\u003Ca href=\"https://doi.org/10.26434/chemrxiv-2025-m6r2j\">https://doi.org/10.26434/chemrxiv-2025-m6r2j\u003C/a>",{"alignment":95},{"alignment":60},{"id":97,"type":55,"data":98,"tunes":100},"LRV8S6L7HB",{"text":99},"For more information about Chemspeed solutions:",{"alignment":101},{"alignment":60},{"id":103,"type":104,"data":105,"tunes":108},"yO9l5Bmlg9","header",{"text":106,"level":107},"Contact us to learn more about this exciting article:\u003Ca href=\"https://www.chemspeed.com/contact/\">https://www.chemspeed.com/contact/\u003C/a>",3,{"alignment":109},{"alignment":60},"2.31.0-rc.7","2026-04-07",{"title":113,"meta_description":114},"Computer Vision for High‑Throughput Materials Synthesis","Learn how computer vision enhances high‑throughput materials synthesis, improves reaction monitoring, and accelerates automated materials discovery.",[116,119],{"id":117,"news_id":44,"industry_id":118},1205,4,{"id":120,"news_id":44,"industry_id":121},1206,16,[123,126,128,131],{"id":124,"news_id":44,"workflow_id":125},1154,8,{"id":127,"news_id":44,"workflow_id":4},1155,{"id":129,"news_id":44,"workflow_id":130},1156,2,{"id":132,"news_id":44,"workflow_id":133},1157,7,{"id":4,"sort":49,"user_created":5,"date_created":135,"user_updated":136,"date_updated":137,"internal_name":138,"header_presentation":49,"cta_text":49,"cta_link":49,"preview":139,"items":140},"2025-07-03T09:39:06.000Z","396bdb39-b8ef-4bbd-ad5d-54f68f2da054","2026-04-01T06:57:00.000Z","Default Header","42627e3f-7447-4f40-8aa6-1aedfb54c7f6",[141,210,322,366,403,447],{"item":142},{"title":143,"style":144,"items":145},"Portfolio","double",[146,188],{"nova_blocks_header_submenu_items_id":147},{"title":148,"items":149},"Scalable Automation",[150,156,162,168,174,181],{"sort":4,"item":151},{"page_title":152,"slug":153,"menu_description":154,"menu_icon":155},"Benchtop Solutions","benchtop-crystal","Space-Saving, High-Impact","breakfast_dining",{"sort":130,"item":157},{"page_title":158,"slug":159,"menu_description":160,"menu_icon":161},"Configurable Solutions","configurable-solutions","Tailored for Your Workflow","edit_square",{"sort":107,"item":163},{"page_title":164,"slug":165,"menu_description":166,"menu_icon":167},"NMR Automation Portfolio","bruker-nmr-automation","E2E automated NMR workflows","location_automation",{"sort":118,"item":169},{"page_title":170,"slug":171,"menu_description":172,"menu_icon":173},"Automated \"Make\" and IR & Raman Workflows ","automated-ir-and-raman-workflows","Spectroscopy Automation","escalator",{"sort":175,"item":176},5,{"page_title":177,"slug":178,"menu_description":179,"menu_icon":180},"QC Packages","qc-packages","Quality Control Solutions","verified_user",{"sort":182,"item":183},6,{"page_title":184,"slug":185,"menu_description":186,"menu_icon":187},"Software","software-and-digitalization","Connect. 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Today, the profile of 'the' optimal DAC adsorbent displaying high CO2 uptake and selectivity as well as fast sorption kinetics remains unknown.\u003C/p>","2026-03-24",[523,525],{"id":524,"news_id":517,"industry_id":118},1304,{"id":526,"news_id":517,"industry_id":121},1305,[528,530,532,534],{"id":529,"news_id":517,"workflow_id":125},1259,{"id":531,"news_id":517,"workflow_id":4},1260,{"id":533,"news_id":517,"workflow_id":130},1261,{"id":535,"news_id":517,"workflow_id":133},1262,{"id":537,"featured_news":492,"news_type":46,"title":538,"url":539,"preview":540,"target_date":541,"picture":49,"industry":542,"workflow":543},14000,"Construction of an Interpretable Machine Learning Model for Yield Prediction and Mechanistic Elucidation Enabled by Global Reaction Route Mapping","construction-of-an-interpretable-machine-learning-model-for-yield-prediction-and-mechanistic-elucidation-enabled-by-global-reaction-route-mapping","\u003Cp>In the past decade, machine learning has emerged as a powerful tool to predict reaction outcomes.\u003C/p>","2026-03-17",[],[],{"left":545,"top":545,"width":546,"height":546,"rotate":545,"vFlip":45,"hFlip":45,"body":547},0,24,"\u003Cpath fill=\"currentColor\" d=\"M3 18v-2h18v2zm0-5v-2h18v2zm0-5V6h18v2z\"/>",{"left":545,"top":545,"width":546,"height":546,"rotate":545,"vFlip":45,"hFlip":45,"body":549},"\u003Cpath fill=\"currentColor\" d=\"m12 15.4l-6-6L7.4 8l4.6 4.6L16.6 8L18 9.4z\"/>",{"left":545,"top":545,"width":546,"height":546,"rotate":545,"vFlip":45,"hFlip":45,"body":551},"\u003Cpath fill=\"currentColor\" d=\"M12 12q-1.65 0-2.825-1.175T8 8t1.175-2.825T12 4t2.825 1.175T16 8t-1.175 2.825T12 12m-8 8v-2.8q0-.85.438-1.562T5.6 14.55q1.55-.775 3.15-1.162T12 13t3.25.388t3.15 1.162q.725.375 1.163 1.088T20 17.2V20z\"/>",{"left":545,"top":545,"width":546,"height":546,"rotate":545,"vFlip":45,"hFlip":45,"body":553},"\u003Cpath fill=\"currentColor\" d=\"m19.6 21l-6.3-6.3q-.75.6-1.725.95T9.5 16q-2.725 0-4.612-1.888T3 9.5t1.888-4.612T9.5 3t4.613 1.888T16 9.5q0 1.1-.35 2.075T14.7 13.3l6.3 6.3zM9.5 14q1.875 0 3.188-1.312T14 9.5t-1.312-3.187T9.5 5T6.313 6.313T5 9.5t1.313 3.188T9.5 14\"/>",1775551709675]