[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82390-en":3,"doc-seo-82390-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82390,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Artificial Intelligence and the Generative Science of Food Formulation","Food formulation requires balancing taste, nutrition, sustainability, and cost, while accounting for biological, physical, chemical, sensory, environmental, economic, and cultural constraints. Traditional development relies on empirical experimentation and expert intuition, which explores only a tiny portion of the vast design space and remains slow. This work proposes a unified generative framework where digital food representations let AI predict, discover, generate, organize, simulate, and optimize formulations. Sustainability and nutrition metrics become explicit design objectives, supported by data, models, benchmarks, and automation to establish computational food design as a rigorous science.","arXiv :2607 .09529v 1 [ cs .CE] 10 Jul 2026  \nArtificial Intelligence  \nand the Generative Science of Food Formulation  \nVahidullah Tac and Ellen Kuhl  \nDepartment of Mechanical Engineering, Stanford University, Stanford, California 94305, USA.  \nContributing authors: [vtac@stanford.edu](vtac@stanford.edu) ; [ekuhl@stanford.edu](ekuhl@stanford.edu) ;  \nAbstract  \nFood formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Artificial intelligence is advancing rapidly across food science, yet most applications remain isolated prediction and optimization tasks rather than parts of a broader scientific framework. Here we define a unified framework for the generative science of food formulation, in which digital food representations enable artificial intelligence to predict, discover, generate, organize, simulate, and optimize. We illustrate this framework through sustainability and nutrition, where generative artificial intelligence transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances are transforming food formulation into a generative science.  \nKeywords: artificial intelligence; food formulation; generative artificial intelligence; computational food design; sustainable foods; personalized nutrition  \n1 MOTIVATION  \nFood formulation is becoming a generative science. Feeding a growing global population while improving human health and reducing environmental impact requires foods that simultaneously satisfy multiple competing objectives, including nutrition, sensory quality, sustainability, affordability, manufacturability, and consumer acceptance [34] . Every recipe therefore represents a complex compromise among biological, physical, environmental, and societal constraints [29] . Unlike most engineered systems, foods must simultaneously satisfy physical, chemical, biological, sensory, nutritional, environmental, economic, and cultural constraints [15] . Traditionally, food scientists have navigated this vast design space through empirical experimentation, expert intuition, and iterative product development [39] .  \nWhile this approach has produced remarkable innovations, it remains inherently slow and explores only a minute fraction of the enormous space of possible food formulations [33] .  \nArtificial intelligence is beginning to transform this paradigm [36] . Predictive models estimate food properties directly from ingredient composition, discovery algorithms uncover scientific relationships hidden within experimental data [54], generative models create entirely new recipes and formulations [21], foundation models organize knowledge across diverse food domains [8], emerging world models simulate how foods evolve during processing and storage [23], and  \nagentic AI systems increasingly support autonomous scientific workflows [7] . Collectively, these advances extend artificial intelligence from predicting the properties of existing foods toward designing entirely new formulations.  \nMore fundamentally, food science itself is becoming increasingly digital [4]: ingredients, nutrition, flavor, texture, sustainability, and shelf life can now be represented computationally through structured data that connect food composition to physical, chemical, sensory, and environmental properties [15] . Once these digital representations become available, artificial intelligence can learn relationships between formulation and function, predict food performance before products exist, and ultimately generate new formulations that satisfy multiple competing objectives [64] . Can food formulation become a generative science? In much the same way that computer-aided design transfo","cbCaiqi6kfJxDX5H","https://ap.wps.com/l/cbCaiqi6kfJxDX5H","pdf",13955396,2,1,20,"English","en",105,"# Motivation\n## From predictive models to generative food science\n## Digital food representations and unified computational frameworks\n## Objectives for sustainability and nutrition","[{\"question\":\"Why is food formulation described as becoming a generative science?\",\"answer\":\"Because it requires meeting multiple competing objectives while AI and digital representations shift food design from evaluating existing recipes toward systematically generating new formulations.\"},{\"question\":\"What is the proposed unified framework for generative science of food formulation?\",\"answer\":\"Digital food representations enable AI to predict, discover, generate, organize, simulate, and optimize, forming a single computational framework for computational food design.\"},{\"question\":\"How do sustainability and nutrition change in the framework?\",\"answer\":\"Environmental and nutritional metrics move from post hoc evaluation criteria to explicit design objectives optimized alongside sensory quality, cost, and manufacturability.\"}]",1784180092,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"artificial-intelligence-and-the-generative-science-of-food-formulation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/artificial-intelligence-and-the-generative-science-of-food-formulation/82390/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","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 food formulation described as becoming a generative science?","Question",{"text":75,"@type":76},"Because it requires meeting multiple competing objectives while AI and digital representations shift food design from evaluating existing recipes toward systematically generating new formulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the proposed unified framework for generative science of food formulation?",{"text":80,"@type":76},"Digital food representations enable AI to predict, discover, generate, organize, simulate, and optimize, forming a single computational framework for computational food design.",{"name":82,"@type":73,"acceptedAnswer":83},"How do sustainability and nutrition change in the framework?",{"text":84,"@type":76},"Environmental and nutritional metrics move from post hoc evaluation criteria to explicit design objectives optimized alongside sensory quality, cost, and 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