[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121089-en":3,"doc-seo-121089-105":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},121089,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly","Amphiphilic molecules spontaneously form self-assembled structures whose shapes depend on molecular structure, temperature, and physical conditions. The functional performance of these structures is determined by their formation process, yet evaluating them requires many procedural steps during molecular simulation workflows. The study develops machine-learning models that extract structural features from mesoscale, non-ordered self-assembled structures using particle-type and coordinate data. Graph neural networks learn local structural information and achieve up to 78.35% classification accuracy for systems up to 4,050 coarse-grained particles, enabling efficient structural analysis without manual feature engineering.","arXiv :2402 . 19019v2 [ cond-mat .soft] 17 Apr 2024  \nARTICLE  \nMachine learning-enabled exploration of mesoscale architectures inamphiphilic-molecule self-assembly  \nTakeo Sudoa , Satoki Ishiaia , Yuuki Ishiwataria , Takahiro Yokoyamaa , Kenji Yasuokaa , and Noriyoshi Araia∗  \na Department of Mechanical Engineering, Keio University, Yokohama, Japan.  \nARTICLE HISTORY  \nCompiled April 18, 2024  \nABSTRACT  \nAmphiphilic molecules spontaneously form self-assembled structures of various  \nshapes depending on their molecular structures, the temperature, and other physical  \nconditions. The functionalities of these structures are dictated by their formations  \nand their properties must be evaluated for reproduction using molecular simulations.  \nHowever, the assessment of such intricate structures involves many procedural steps.  \nThis study investigates the potential of machine-learning models to extract struc  \ntural features from mesoscale non-ordered self-assembled structures, and suggests a  \nmethodology in which machine-learning models for the structural analysis of self  \nassembled structures are trained on particle types and coordinate data. In the pro  \nposed approach, graph neural networks are utilised to extract local structural data  \nfor analysis. In simulations using several hundred self-assembled structures of up to  \n4050 coarse-grained particles, local structures are successfully extracted and classi  \nfied with up to 78 .35% accuracy. As the machine-learning models learn structural  \ncharacteristics without the need for human-made feature engineering, the proposed  \nmethod has important potential applications in the field of materials science.  \nKEYWORDS  \nGraph neural network, Self-assembly, Machine learning, structural analysis,  \ndissipative particle dynamics  \n1. Introduction  \nAmphiphilic molecules are versatile substances used in various industries and products, such as cosmetics, detergents, and pharmaceuticals[1–3] . These molecules are also gaining attention in the fields of materials science, chemical engineering, and medical materials because of their easy-to-control synthesis of chemical structures and their ability to self-assemble; that is, to form distinctive functional structures without human intervention. The functionalities of the self-assembled structures formed by amphiphilic molecules, such as micelles and vesicles, are closely related to their formations. Micelles are commonly used in detergents to render oil-based impurities soluble. Vesicles, including liposomes, are frequently used as pharmaceutical carriers in biological systems[4–6] . Notably, amphiphilic-molecule functionalities are not solely derived from their molecular structures but also from their self-assembled structures. Therefore, predicting and evaluating these structures are essential for realising  \n∗ Corresponding author. Email: [arai@mech.keio.ac.jp](arai@mech.keio.ac.jp); Fax: +81 45 566 1495; Tel: +81 45 566 1846  \nhighly functional materials and manipulating their functionalities; however, a systematic framework has yet to be established. At present, the development of functional materials using amphiphilic molecules involves repeated experiments based on trial and error.  \nMolecular motion can be simulated by virtually manipulating each atom using a computer, and this approach is gaining attention for its potential to predict the formation of self-assembled structures and their resulting functionalities in soft matter based on molecular structures[7,8] . However, molecular dynamics simulation[9,10], which simulates the motion of each atom in a system, is limited by computational constraints when simulating mesoscale structures such as self-assembled structures in soft matter. Dissipative particle dynamics (DPD) simulation[11–13], which aggregatesa given number of atoms into a single bead and simulates their behaviour collectively, facilitates simulations at such scales. DPD simulations can reproduce self-assembled stru","cbCaif2woQe8Wbk2","https://ap.wps.com/l/cbCaif2woQe8Wbk2","pdf",1908154,1,26,"English","en",105,"# Introduction\n## Background on amphiphilic molecule self-assembly\n## Simulation approaches and their limitations\n## Prior quantitative evaluations and motivation\n# Methodology and proposed ML approach\n## Graph neural networks for local structural extraction\n## Training inputs: particle types and coordinate data\n# Results\n## Dataset size and system scale\n## Classification accuracy\n# Conclusion\n## Implications for materials science applications","[{\"question\":\"Why is evaluating self-assembled structures of amphiphilic molecules challenging?\",\"answer\":\"Their structures depend on molecular structure and physical conditions, but quantitative assessment through simulation involves many procedural steps and lacks a systematic framework. Complex configurations also cannot be easily evaluated visually.\"},{\"question\":\"What is the core idea of the proposed machine-learning method?\",\"answer\":\"The approach trains machine-learning models to extract structural features from mesoscale, non-ordered self-assembled structures using particle types and coordinate data. Graph neural networks are used to capture local structural information for analysis.\"},{\"question\":\"How accurate is the structural classification in the simulations?\",\"answer\":\"In simulations across several hundred self-assembled structures with up to 4,050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35% accuracy.\"}]","Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly | PDF",1785733662,66,{"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},"machine-learning-enabled-exploration-of-mesoscale-architectures-in-amphiphilic-molecule-self-assembly","",{"@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/machine-learning-enabled-exploration-of-mesoscale-architectures-in-amphiphilic-molecule-self-assembly/121089/",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","2026-08-03",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 is evaluating self-assembled structures of amphiphilic molecules challenging?","Question",{"text":75,"@type":76},"Their structures depend on molecular structure and physical conditions, but quantitative assessment through simulation involves many procedural steps and lacks a systematic framework. Complex configurations also cannot be easily evaluated visually.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core idea of the proposed machine-learning method?",{"text":80,"@type":76},"The approach trains machine-learning models to extract structural features from mesoscale, non-ordered self-assembled structures using particle types and coordinate data. Graph neural networks are used to capture local structural information for analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the structural classification in the simulations?",{"text":84,"@type":76},"In simulations across several hundred self-assembled structures with up to 4,050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35% accuracy.","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":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]