[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127285-en":3,"doc-seo-127285-105":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},127285,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","What can machine learning help with microstructure-informed materials modeling and design? - A review overview","Machine learning techniques are presented as effective tools for microstructure-informed materials modeling, where tailoring macroscopic properties depends on complex microstructural features. The review summarizes ML-assisted and data-driven advances across microstructure characterization and reconstruction, multiscale simulation, links between process–microstructure–property, and microstructure optimization with inverse design. It distills best practices, outlines future research directions, and provides foundational guidance on ML concepts and microstructure descriptors to reduce interdisciplinary barriers.","Machine learning (ML) techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current ML-assisted and data-driven advancementsin this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on ML, microstructure descriptors, and ML-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of ML-based modeling and design of microstructured materials.  \nIntroduction  \nMicrostructured materials manifest in diverse engineering scenarios in forms of polycrystalline microstructures, inclusionmatrix composites, bicontinuous composites, porous structures, 1 etc. Their macroscopic properties strongly depend on the underlying microstructural features. This presents an avenue for achieving tailored macroscopic properties, which are unattainable in the base materials. Thus, comprehending the quantitative impact of microstructures on macroscopic properties is desired. The fabrication or synthesis of microstructured materials is a complex process subjected to various process parameters. These parameters significantly affect the resultant microstructures, and thereby the corresponding macroscopic properties. Thus, the design, fabrication, and application of microstructured materials necessitate a deep understanding of the interplay between process and microstructure, microstructure and property, and, ultimately, direct process–property relations. Traditional experimental and numerical methods, though valuable, cannot efficiently tackle these challenging tasks.  \nIn recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative forces, driving profound economic and social changes. They have  \nbecome pivotal technologies in various research domains, including materials science and engineering.2 The integration of ML and data-driven techniques into scientific research methodologies has given rise to what is known as the fourth research paradigm,3 extending the third paradigm of computational science . This paradigm shift signifies a departure from traditional scientific approaches, embracing the power of data-driven insights and predictive capabilities facilitated by ML. A number of review papers have illuminated the remarkable progress achieved through the application of ML methods in different scenarios in the broad field of material sciences and mechanics. These include materials design,4–7 atomistic simulations,2 , 8 multiscale modeling and simulation,9 mechanics of materials, 10–12 etc. These reviews collectively demonstrate the efficiency and effectiveness of ML methods in addressing a spectrum of challenging problems in material design, modeling, and engineering.  \nAs summarized in Table I, ML can assist materials modeling in terms of constitutive surrogates, energy functional surrogates or field predictors, in alignment with continuum mechanics and thermodynamics. However, the existing research and literature articles are mostly dedicated to  \nXiang-Long Peng, Mechanics of Functional Materials Division, Institute of Materials Science, Technische Universität Darmstadt, Darmstadt, Germany; [xianglong.peng@tu-darmstadt.de](xianglong.peng@tu-darmstadt.de)[ ](xianglong.peng@tu-darmstadt.de)Mozhdeh Fathidoost, Mechanics of Functional Materials Division, Institute of Materials Science, Technische Universität Darmstadt, Darmstadt, Germany ","cbCaitw1BB9nW2Ev","https://ap.wps.com/l/cbCaitw1BB9nW2Ev","pdf",5576976,1,19,"English","en",105,"# Introduction\n## Microstructured materials and process–microstructure–property relations\n## Fourth research paradigm and role of ML in materials science\n## How ML supports materials modeling within continuum frameworks\n## Scope of the review: modeling, correlation, optimization, and inverse design","[{\"question\":\"Why are microstructure-informed materials modeling tasks challenging?\",\"answer\":\"Because microstructures are intricate and diverse, their descriptors and local properties are difficult to use as direct variables, and the underlying physics implications are often unknown. This limits conventional continuum approaches.\"},{\"question\":\"How can machine learning assist materials modeling in the framework of continuum mechanics and thermodynamics?\",\"answer\":\"ML can support modeling through constitutive surrogates, energy functional surrogates, or field predictors. These approaches align with continuum mechanics and thermodynamics while addressing complex microstructure effects.\"},{\"question\":\"What topics does the review cover regarding ML-based microstructured materials?\",\"answer\":\"It covers microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, and microstructure optimization and inverse design, along with best practices and future research directions.\"}]","What can machine learning help with microstructure-informed materials modeling and design? - A review overview | PDF",1785938101,48,{"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},"what-can-machine-learning-help-with-microstructure-informed-materials-modeling-and-design-a-review-overview","",{"@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/what-can-machine-learning-help-with-microstructure-informed-materials-modeling-and-design-a-review-overview/127285/",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 are microstructure-informed materials modeling tasks challenging?","Question",{"text":75,"@type":76},"Because microstructures are intricate and diverse, their descriptors and local properties are difficult to use as direct variables, and the underlying physics implications are often unknown. This limits conventional continuum approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How can machine learning assist materials modeling in the framework of continuum mechanics and thermodynamics?",{"text":80,"@type":76},"ML can support modeling through constitutive surrogates, energy functional surrogates, or field predictors. These approaches align with continuum mechanics and thermodynamics while addressing complex microstructure effects.",{"name":82,"@type":73,"acceptedAnswer":83},"What topics does the review cover regarding ML-based microstructured materials?",{"text":84,"@type":76},"It covers microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, and microstructure optimization and inverse design, along with best practices and future research directions.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]