[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123475-en":3,"doc-seo-123475-105":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},123475,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Data-driven inverse design of graphene Kirigami with negative Poisson's ratio using machine learning and genetic algorithms","Graphene’s mechanical behavior, especially Poisson’s ratio, can be tuned by introducing perforation-based structural defects, enabling more programmable flexible nanoelectronic components. A data-driven framework combining machine learning with genetic algorithms is proposed to inversely design rectangular perforated graphene Kirigami architectures targeting negative Poisson’s ratio. Molecular dynamics simulations generate Poisson-ratio datasets across varying perforation geometries. Four ML regressors are trained; XGBoost achieves the best accuracy and generalization. Feature importance indicates strong sensitivity to perforation interspacing, while aspect ratio and unit length are weaker. The optimized XGBoost model is coupled with GA to produce configurations meeting targeted NPR, and the ML-GA workflow is validated via additional MD, demonstrating efficient handling of complex materials design problems.","Han, T. , Zhang, S. , Zhang, X. , & Scarpa, F. (2025) . Data-driven inverse design of graphene Kirigami with negative Poisson's ratio using machine learning and genetic algorithms. Diamond & Related Materials, 160, Article 112991. [https://doi.org/10.1016/j.diamond.2025.112991](https://doi.org/10.1016/j.diamond.2025.112991)  \nPeer reviewed version  \nLicense (if available): CC BY  \nLink to published version (if available):  \n10.1016/j.diamond.2025.112991  \nLink to publication record on the Bristol Research Portal  \nPDF-document  \nThis is the accepted author manuscript (AAM) of the article which has been made Open Access under the University of Bristol's Scholarly Works Policy. The final published version (Version of Record) can be found on the publisher's website. The copyright of any third-party content, such as images, remains with the copyright holder.  \nUniversity of Bristol – Bristol Research Portal  \nGeneral rights  \nThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: [http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/](http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/)  \nData-driven inverse design of graphene Kirigami with negative Poisson's ratio using machine learning and genetic algorithms  \nTongwei Han a, *, Suncheng Zhang a, Xiaoyan Zhang b, Fabrizio Scarpa c, * a Faculty of Civil Engineering and Mechanics, Jiangsu University, No. 301 Xuefu Road, Zhenjiang, Jiangsu 210013, People’s Republic of China  \nb School of Chemistry and Chemical Engineering, Jiangsu University, No. 301 Xuefu Road, Zhenjiang, Jiangsu 210013, People’s Republic of China  \nc Bristol Composites Institute, School of Civil, Aerospace and Design Engineering (CADE), University of Bristol, Bristol BS8 1TR, United Kingdom  \nAbstract  \nGraphene's mechanical properties, particularly its Poisson's ratio, can be tuned by introducing structural defects like perforations, which enhances its potential for flexible nanoelectronics. We propose a framework that integrates machine learning (ML) with genetic algorithms (GA) to efficiently predict and inversely design rectangular perforated graphene Kirigami structures. The approach specifically targets configurations exhibiting a negative Poisson's ratio (NPR) . Unlike conventional rotating rigid-unit systems, the NPR effect in this case results from the combined influence of in-plane rotation and out-of-plane deformation. Molecular dynamics (MD) simulations were performed to generate a dataset of Poisson's ratios for graphene Kirigami structures with varying perforation geometries. Four machine learning models, including Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), were trained to predict Poisson's ratios. XGBoost exhibited superior accuracy and generalization. Feature importance analysis revealed that perforation interspacing (IS) strongly influences Poisson’s ratio, whereas perforation aspect ratio (AR) and unit length (L) exert weaker effects. The optimized XGBoost model was integrated with a GA for inverse design, successfully generating graphene Kirigami configurations with targeted negative  \n* Corresponding author.  \nE-mail address: [twhan@ujs.edu.cn](twhan@ujs.edu.cn) (Tongwei Han), [f.scarpa@bristol.ac.uk](f.scarpa@bristol.ac.uk) (Fabrizio Scarpa)  \nPoisson's ratios. The ML-GA framework was validated through MD simulations, showcasing its effectiveness in tackling complex material design challenges. This work highlights the potential of integrating machine learning and genetic algorithms for efficiently optimizing graphene and other advanced materials.  \nKeywords：machine learning; inverse design; graphene; Kirigami; negative Poisson's ratio; molecular dynamics;  \n1. Introduction  \nGraphene has become a central focus in materials science and condensed matter phys","cbCaiarnQwsXrSxr","https://ap.wps.com/l/cbCaiarnQwsXrSxr","pdf",1650374,1,24,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Related work on negative Poisson’s ratio in graphene\n## Gap in existing design approaches\n# Proposed data-driven ML-GA framework","[{\"question\":\"What strategy does the document propose for inverse design of graphene Kirigami?\",\"answer\":\"It integrates machine learning models with genetic algorithms to efficiently predict Poisson’s ratio and inversely search for perforated graphene Kirigami geometries that achieve a targeted negative Poisson’s ratio.\"},{\"question\":\"How was the dataset for training the machine learning models generated?\",\"answer\":\"Molecular dynamics simulations were used to generate a dataset of Poisson’s ratios for graphene Kirigami structures with different perforation geometries.\"},{\"question\":\"Which model performs best and what geometric factor matters most?\",\"answer\":\"XGBoost shows superior accuracy and generalization. Feature importance analysis indicates perforation interspacing strongly influences Poisson’s ratio, while perforation aspect ratio and unit length have weaker effects.\"}]","Data-driven inverse design of graphene Kirigami with negative Poisson's ratio using machine learning and genetic algorithms | PDF",1785816724,60,{"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},"data-driven-inverse-design-of-graphene-kirigami-with-negative-poissons-ratio-using-machine-learning-and-genetic-algorithms","",{"@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/data-driven-inverse-design-of-graphene-kirigami-with-negative-poissons-ratio-using-machine-learning-and-genetic-algorithms/123475/",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","2026-08-04",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 strategy does the document propose for inverse design of graphene Kirigami?","Question",{"text":75,"@type":76},"It integrates machine learning models with genetic algorithms to efficiently predict Poisson’s ratio and inversely search for perforated graphene Kirigami geometries that achieve a targeted negative Poisson’s ratio.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset for training the machine learning models generated?",{"text":80,"@type":76},"Molecular dynamics simulations were used to generate a dataset of Poisson’s ratios for graphene Kirigami structures with different perforation geometries.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and what geometric factor matters most?",{"text":84,"@type":76},"XGBoost shows superior accuracy and generalization. Feature importance analysis indicates perforation interspacing strongly influences Poisson’s ratio, while perforation aspect ratio and unit length have weaker effects.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"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"]