[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119235-en":3,"doc-seo-119235-105":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":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},119235,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning to identify structural motifs in asphaltenes","Asphaltenes are organic compounds that aggregate in crude oil and exhibit two dominant molecular architectures: archipelago and continental. Continental motifs feature a single uniform island of aromatic rings, while archipelago motifs connect aromatic cores through aliphatic chains. Because asphaltene structures vary globally with geography, consistent classification is difficult. This work applies image-based supervised machine learning, using a ResNet-50 neural network, to binary-classify asphaltenes into continental and archipelago motifs.","Biology and Chemistry Faculty Publications and Presentations  \nDepartment of Biology and Chemistry  \n1-2024  \nMachine learning to identify structural motifs in asphaltenes Arun K. Sharma  \nSelsela Arsala  \nJames Brady Madison Franke Shelby Franke  \nSee next page for additional authors  \nFollow this and additional works at: [https://digitalcommons.csumb.edu/biochem_fac](https://digitalcommons.csumb.edu/biochem_fac)  \nThis Article is brought to you for free and open access by the Department of Biology and Chemistry at Digital Commons @ CSUMB. It has been accepted for inclusion in Biology and Chemistry Faculty Publications and Presentations by an authorized administrator of Digital Commons @ CSUMB. For more information, please contact [digitalcommons@csumb.edu](digitalcommons@csumb.edu).  \nAuthors  \nArun K. Sharma, Selsela Arsala, James Brady, Madison Franke, Shelby Franke, Supreet Gandhok, SimonOlivier Gingras, Ana Gomez, Katelyn Huie, Kayla Katz, Samantha Kozlo, Mateo Longoria, Levi Molnar, Nathaly Peña, and Sarina Regis  \nResults in Chemistry 7 (2024) 101551  \nContents lists available at ScienceDirect  \nResults in Chemistry  \njournal [homepage: www.sciencedirect.com/journal/results-in-chemistry](homepage: www.sciencedirect.com/journal/results-in-chemistry)  \n| Machine learning to identify structural motifs in asphaltenes |  |  |  |\n| --- | --- | --- | --- |\n| *\u003Cbr>Arun K. Sharma , Selsela Arsala , James Brady , Madison Franke , Shelby Franke , Supreet Gandhok , Simon-Olivier Gingras , Ana Gomez , Katelyn Huie , Kayla Katz , Samantha Kozlo , Mateo Longoria , Levi Molnar , Nathaly Pe˜na , Sarina Regis\u003Cbr>Department of Biology and Chemistry, California State University Monterey Bay, Seaside, CA 93955, United States |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Asphaltenes Machine learning Image recognition\u003Cbr>Molecular topology Deep learning |  | Asphaltenes are organic compounds that aggregate in crude oil with two dominant molecular architectures: archipelago and continental. Continental architectures possess a single uniform island structure composed of aromatic rings in contrast to archipelago architectures with aromatic cores interconnected through aliphatic chains. The structural composition of asphaltenes varies globally due to geographical differences, posing challenges in their classification due to a lack of uniformity. This study is the first known exploration of using imagebased supervised machine learning, particularly the ResNet-50 neural network, for the binary classification of asphaltenes into continental and archipelago motifs. 255 continental and archipelago models underwent structural augmentations to create a sample size of 1,530 asphaltene structures that is robust enough for accurate results in both the training and testing portions of the machine learning. These augmentations included the repeated addition of carbons until a complete pentane chain was added to a specified carbon on each asphaltene structure. Using Mathematica, supervised ResNet-50 image-based classification was used on both original and augmented structure datasets to classify as either archipelago or continental. The classification was also implemented using topological similarity searching for association between atoms and the distance between them for further molecule identification. This study demonstrates the surprising effectiveness of image-based classification compared to traditional topological feature-based methods. Our results reveal that deep learning techniques, especially image-based approaches, provide novel and insightful ways to differentiate complex molecular structures like asphaltenes, challenging the traditional reliance on topological features alone. This research opens new avenues in chemical analysis and molecular characterization, highlighting the potential of machine learning in complex molecular systems. |  |\n\nIntroduction  \nAsphaltenes, a complex component of crude oil, present signifi","cbCaifAFhy4LQIaI","https://ap.wps.com/l/cbCaifAFhy4LQIaI","pdf",906432,1,12,"English","en",105,"# Introduction\n## Asphaltenes and classification challenges\n## Role of machine learning in chemical research\n## Supervised learning for pattern recognition\n## Deep learning and ResNet-50","[{\"question\":\"What molecular architectures of asphaltenes does the study focus on?\",\"answer\":\"It targets two dominant architectures: continental and archipelago. Continental motifs contain a single uniform aromatic-ring island, while archipelago motifs connect aromatic cores via aliphatic chains.\"},{\"question\":\"How does the study perform asphaltene classification?\",\"answer\":\"It uses image-based supervised machine learning with a ResNet-50 neural network for binary classification into continental versus archipelago motifs.\"},{\"question\":\"Why is the dataset augmented in this research?\",\"answer\":\"Structural augmentations increase the sample size to make training and testing robust. The method repeatedly adds carbons so a complete pentane chain is incorporated into each asphaltene structure.\"}]","Machine learning to identify structural motifs in asphaltenes | PDF",1785723229,30,{"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},"machine-learning-to-identify-structural-motifs-in-asphaltenes","",{"@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-to-identify-structural-motifs-in-asphaltenes/119235/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What molecular architectures of asphaltenes does the study focus on?","Question",{"text":75,"@type":76},"It targets two dominant architectures: continental and archipelago. Continental motifs contain a single uniform aromatic-ring island, while archipelago motifs connect aromatic cores via aliphatic chains.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study perform asphaltene classification?",{"text":80,"@type":76},"It uses image-based supervised machine learning with a ResNet-50 neural network for binary classification into continental versus archipelago motifs.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the dataset augmented in this research?",{"text":84,"@type":76},"Structural augmentations increase the sample size to make training and testing robust. 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