[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121883-en":3,"doc-seo-121883-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},121883,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","When Machine Learning Meets 2D Materials - A Review","The paper reviews how machine learning can accelerate the discovery and design of functional 2D materials and engineered heterostructures. It addresses the challenge posed by the immense multi-dimensional parameter space and massive datasets that make exhaustive experimental sampling impractical. By shifting from ab initio calculations to data-driven exploration, ML—covering deep learning, neural networks, and support vector methods—enables more efficient prediction, autonomous experimentation, and improved handling of computational resource limits.","REVIEW  \n[www.advancedscience.com](www.advancedscience.com)  \nWhen Machine Learning Meets 2D Materials: A Review  \nBin Lu, Yuze Xia, Yuqian Ren, Miaomiao Xie, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T. S. Wee, Wilfred G. van der Wiel, Wen Zhang,* and Ping Kwan Johnny Wong*  \nThe availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex,  \nresource-intensive experimentation, which not only challenges the current state ofthe art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also anew paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.  \ntheoretical analysis, and application. As with other fields, materials science has gone through a shift from the traditional paradigm of experimental science to the modern paradigm of data exploration. [1] Against this backdrop, theoretical approaches, such as density functional theory (DFT) and molecular dynamics (MD), have been developed to analyze microstructures of materials and guide future research. However, due to the growing amount of data and limited computational resources, these methods are becoming increasingly time-consuming. To overcome this challenge, a paradigm shift from ab initio calculations to extensive data exploration has occurred in materials science, with machine learning (ML) being an effective means of realizing this shift. ML encompasses a wide array of mathematical algorithmsand model systems, such as deep learning (DL), deep neural network (DNN), and support vector machine (SVM) algorithms,  \n1. Introduction  \nSince the discovery of graphene, 2D materials have attracted much attention from researchers across the globe, with significant achievements in their preparation, characterization,  \netc. The goal of ML is to enable computers to learn from data, improve their performance on tasks, and make accurate predictions or decisions without explicit programming. There exists a large body of literature on ML algorithms, and more in-depth discussions can be found elsewhere. [2,3] ML has been widely adopted  \nB. Lu, Y. Xia, Y. Ren, M. Xie, L. Zhou, W. Zhang, P. K. J. Wong  \nARTIST Lab for Artificial Electronic Materials and Technologies, School of Microelectronics  \nNorthwestern Polytechnical University Xi’an 710072, P. R. China  \nE-mail: [zhang.wen@nwpu.edu.cn](zhang.wen@nwpu.edu.cn); [pingkwanj.wong@nwpu.edu.cn](pingkwanj.wong@nwpu.edu.cn)  \nB. Lu, Y. Xia, Y. Ren, M. Xie, L. Zhou, W. Zhang, P. K. J. Wong  \nYangtze River Delta Research Institute of Northwestern Polytechnical University  \nTaicang 215400, P. R. China  \nG. Vinai  \nInstituto Officina dei Materiali (IOM)-CNR Laboratorio TASC  \nTrieste I-34149, Italy  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/advs.202305277](https://doi.org/10.1002/advs.202305277)[ ](https://doi.org/10.1002/advs.202305277)© 2024 The Authors. Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original ","cbCaisWVHBPQHW9R","https://ap.wps.com/l/cbCaisWVHBPQHW9R","pdf",31286910,1,40,"English","en",105,"# Introduction\n## Machine learning as a data-driven paradigm\n## Motivation: high-dimensional space and data scale\n## Machine learning algorithms and model systems\n## Review scope and emerging opportunities","[{\"question\":\"Why is machine learning considered valuable for studying 2D materials?\",\"answer\":\"Because the parameter space is extremely high-dimensional and datasets are massive, making exhaustive sampling and resource-intensive experimentation impractical. ML offers a cheaper, more efficient alternative to traditional computational strategies.\"},{\"question\":\"What problem does the paper highlight about existing theoretical approaches?\",\"answer\":\"Methods such as density functional theory and molecular dynamics become increasingly time-consuming as data grows and computational resources remain limited.\"},{\"question\":\"What does the review focus on regarding ML-enabled research?\",\"answer\":\"It summarizes recent progress, discusses challenges, and highlights emerging opportunities for autonomous experimentation and machine-assisted design of functional 2D materials and heterostructures.\"}]","When Machine Learning Meets 2D Materials - A Review | PDF",1785807453,101,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"when-machine-learning-meets-2d-materials-a-review","",{"@graph":36,"@context":86},[37,54,69],{"@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/when-machine-learning-meets-2d-materials-a-review/121883/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is machine learning considered valuable for studying 2D materials?","Question",{"text":76,"@type":77},"Because the parameter space is extremely high-dimensional and datasets are massive, making exhaustive sampling and resource-intensive experimentation impractical. ML offers a cheaper, more efficient alternative to traditional computational strategies.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem does the paper highlight about existing theoretical approaches?",{"text":81,"@type":77},"Methods such as density functional theory and molecular dynamics become increasingly time-consuming as data grows and computational resources remain limited.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the review focus on regarding ML-enabled research?",{"text":85,"@type":77},"It summarizes recent progress, discusses challenges, and highlights emerging opportunities for autonomous experimentation and machine-assisted design of functional 2D materials and heterostructures.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":21,"slug":119},7,"Healthcare","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]