[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126672-en":3,"doc-seo-126672-105":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},126672,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning - Nature Communications 14(1) - 2023","Designing metamaterials with customizable architectures enables mechanical responses beyond those of constituent bulk materials, expressed through response curves such as quasi-static stress–strain behavior. Existing inverse design methods remain limited in realizing full complex target behaviors due to multiple objectives, nonlinear mechanics, and manufacturing errors dependent on the fabrication process. The study reports a rapid inverse design methodology using generative machine learning with desktop additive manufacturing to create nearly all uniaxial compressive stress–strain curve cases while accounting for printing errors, achieving about 90% fidelity between target and experimental results.","Lawrence Berkeley National Laboratory  \nLBL Publications  \nTitle  \nRapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning.  \nPermalink  \n[https://escholarship.org/uc/item/4q53q0fs](https://escholarship.org/uc/item/4q53q0fs)  \nJournal  \nNature Communications, 14(1)  \nAuthors  \nHa, Chan  \nYao, Desheng Xu, Zhenpenget al.  \nPublication Date  \n2023-09-18  \nDOI  \n10.1038/s41467-023-40854-1  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle [https://doi.org/10.1038/s41467-023-40854-1](https://doi.org/10.1038/s41467-023-40854-1)  \nRapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning  \nReceived: 29 November 2022  \n\n| Accepted: 11 August 2023 |\n| --- |\n|  |\n| Check for updates |\n\nChan Soo Ha 1,8, Desheng Yao 2,3,8, Zhenpeng Xu2,3, Chenang Liu4, Han Liu 5, Daniel Elkins1,6, Matthew Kile1, Vikram Deshpande 7 , Zhenyu Kong 6 , Mathieu Bauchy3  & Xiaoyu (Rayne) Zheng 1,2,3   \nDesigning and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90%fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.  \nThe intrinsic mechanical behavior of bulk materials (e.g., metals, ceramics, polymers) can be experimentally characterized by the application of force and the measurement of the resulting deformation, yielding stress‒strain curves. For example, under tensile loading, the mechanical behavior of brittle materials such as ceramics is characterized by a stress–strain curve with a linear region followed by a sharp termination; elastomers display superelasticity, characterized by a rapidly rising concave-up stress‒strain curve without a noticeable linear region. For homogenous materials such as metals, ceramics, and polymers, responses to loading are dictated by intrinsic microstructure, such as crystal structure, atomic bonding, and the size and mass of the constituent molecules/atoms, in addition to the presence  \nof stochastic microscopic defects. As a result, there is little room to tailor these materials’ responses to loads besides altering the intrinsic microstructure of the base materials.  \nAdditive manufacturing (AM) allows mechanical properties to be tailored in ways that are impossible in bulk materials, via the microarchitecture design of three-dimensional (3D) metamaterials. These materials can exhibit unusual properties such as negative Poisson’s ratio1–3, negative compressibility4,5, ultralightness and ultrastiffness, shape recoverability6–8, and multiple stable states9–11. These architected materials achieve previously unattainable region in the materialselection chart (e.g., so-called Ashby charts of density vs. Young’s modulus or strength)12,13. Architected materials manifesting these  \n1Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, USA. 2Department of Material Science and Engineering, University of California, Berkeley, CA, USA. ","cbCaiq2xVyHP3SbK","https://ap.wps.com/l/cbCaiq2xVyHP3SbK","pdf",6925701,1,12,"English","en",105,"# Rapid inverse design of metamaterials through machine learning\n## Motivation and limitations of existing inverse design approaches\n## Proposed rapid methodology with generative machine learning and desktop additive manufacturing\n## Experimental outcomes and fidelity between target and measured behavior\n## Background: mechanical characterization and rationale for metamaterial microarchitectures","[{\"question\":\"What is the core goal of the proposed work on inverse design?\",\"answer\":\"Create metamaterial designs that reproduce prescribed mechanical behaviors, specifically uniaxial compressive stress–strain response curves, rather than only matching limited property values.\"},{\"question\":\"Why do existing inverse design approaches struggle to achieve full desired behaviors?\",\"answer\":\"They face challenges from multiple design objectives, nonlinear mechanical behavior, non-unique mapping between design and response, and process-dependent manufacturing errors and defects.\"},{\"question\":\"How does the method account for errors caused by additive manufacturing?\",\"answer\":\"It leverages generative machine learning alongside desktop additive manufacturing, incorporating process-dependent printing errors so that designed curves remain close to experimentally measured results.\"}]","Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning - Nature Communications 14(1) - 2023 | PDF",1785934153,30,{"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},"rapid-inverse-design-of-metamaterials-based-on-prescribed-mechanical-behavior-through-machine-learning-nature-communications-141-2023","",{"@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/rapid-inverse-design-of-metamaterials-based-on-prescribed-mechanical-behavior-through-machine-learning-nature-communications-141-2023/126672/",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},"What is the core goal of the proposed work on inverse design?","Question",{"text":75,"@type":76},"Create metamaterial designs that reproduce prescribed mechanical behaviors, specifically uniaxial compressive stress–strain response curves, rather than only matching limited property values.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why do existing inverse design approaches struggle to achieve full desired behaviors?",{"text":80,"@type":76},"They face challenges from multiple design objectives, nonlinear mechanical behavior, non-unique mapping between design and response, and process-dependent manufacturing errors and defects.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method account for errors caused by additive manufacturing?",{"text":84,"@type":76},"It leverages generative machine learning alongside desktop additive manufacturing, incorporating process-dependent printing errors so that designed curves remain close to experimentally measured results.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]