[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120365-en":3,"doc-seo-120365-105":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},120365,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning for asphaltene polarizability - Evaluating molecular descriptors","Asphaltenes are complex polycyclic organic molecules in crude oil whose structural heterogeneity drives aggregation and precipitation and complicates prediction of key physicochemical properties, including molecular polarizability. This study applies machine learning to predict isotropic polarizability using two molecular-descriptor sets (WHIM and GETAWAY). A dataset of 255 asphaltene structures is analyzed with stratified sampling into 10 independent 80/20 train-test splits. Wolfram Language’s Predict AutoML framework evaluates multiple algorithms, with GETAWAY-based models performing best in 9/10 splits and achieving significantly lower mean absolute deviation than WHIM.","Digital Chemical Engineering 15 (2025) 100244  \nContents lists available at ScienceDirect Digital Chemical Engineering  \njournal [homepage: www.elsevier.com/locate/dche](homepage: www.elsevier.com/locate/dche)  \n| Original Article\u003Cbr>Machine learning for asphaltene polarizability: Evaluating molecular descriptors\u003Cbr>*\u003Cbr>Arun K. Sharma , Owen McMillan , Selsela Arsala , Supreet Gandhok , Rylend Young\u003Cbr>Department of Biology, Agriculture, and Chemistry, California State University Monterey Bay, Seaside, CA, 93955, USA |  |  |\n| --- | --- | --- |\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 Polarizability Prediction Parameters |  | Asphaltenes are complex polycyclic organic molecules in crude oil that readily aggregate and precipitate under varying thermodynamic conditions. Their structural heterogeneity influences key physicochemical properties, including solubility, stability, and reactivity. Molecular polarizability, a crucial property governing intermolecular interactions and electronic behavior, remains challenging to predict due to this structural diversity. This study employs machine learning models to predict isotropic polarizability using two sets of molecular descriptors: WHIM and GETAWAY. A dataset of 255 asphaltene structures was analyzed using stratified sampling, generating 10 independent training (80 %) and testing (20 %) splits. The Wolfram Language’s Predict function evaluated multiple machine learning algorithms—including Random Forest, Decision Tree, Gradient Boosted Trees, Nearest Neighbors, Linear Regression, Gaussian Process, and Neural Network—through an automated model selection process, serving as an AutoML framework. Linear regression was the best-performing model in 9 out of 10 splits for GETAWAY descriptors. GETAWAY-based models achieved an average mean absolute deviation of 0.0920 ± 0.0030 and standard deviation of 0.113 ± 0.004, significantly outperforming WHIM-based models (MAD = 0.173 ± 0.007, STD = 0.224 ± 0.008) with paired t-tests confirming statistical significance (p \u003C 0.001). While R² values were reported, their interpretability was limited by heterogeneity and narrow property ranges in some test sets. These findings demonstrate the effectiveness of AutoML-guided approaches for predicting molecular properties and identify GETAWAY descriptors as a robust, efficient basis for polarizability prediction. Accurate prediction of polarizability is essential for modeling intermolecular forces and improving force field design in petroleum and materials chemistry, issues that are central to industrial and chemical applications. |\n\nIntroduction  \nAsphaltenes (Speight, 2004; Wiehe and Liang, 1996) are complex polycyclic aromatic hydrocarbons that influence crude oil stability, viscosity, and aggregation behavior. Asphaltenes exhibit significant structural heterogeneity, consisting of diverse molecular architectures that influence their physicochemical properties. Two primary structural motifs, archipelago and continental, define their molecular organization. (Mullins et al., 2017) Continental structures feature a single, fused polycyclic aromatic core, while archipelago structures are composed of multiple smaller aromatic cores interconnected by aliphatic linkages.(Yen, 2000) Figs. 1 and 2 represent typical structures that belong to each category. This variability in architecture affects asphaltene solubility, aggregation behavior, and interactions with surrounding molecules. The heterogeneity of asphaltenes arises from differences in crude oil origin,  \nmaturation processes, and environmental conditions, leading to a broad distribution of molecular weights, functional groups, and heteroatom content. (Chilingarian and Yen, 2000; Groenzin and Mullins, 2000; Barrera et al., 2013) Understanding these structural variations is essential for accurately modeling asphaltene behavior in petroleum systems and developing predictive tools for their physicochemical propert","cbCaikDM5nMB5gWK","https://ap.wps.com/l/cbCaikDM5nMB5gWK","pdf",1277277,1,7,"English","en",105,"# Introduction\n## Asphaltene structural heterogeneity and motifs\n## Isotropic polarizability and its role in properties and interactions","[{\"question\":\"Why is predicting asphaltene polarizability difficult?\",\"answer\":\"Asphaltenes have strong structural heterogeneity across molecular architectures, which affects properties and makes polarizability harder to model reliably.\"},{\"question\":\"What data splitting strategy and dataset size were used?\",\"answer\":\"The study analyzes 255 asphaltene structures using stratified sampling to create 10 independent training (80%) and testing (20%) splits.\"},{\"question\":\"Which molecular descriptors produced better polarizability predictions?\",\"answer\":\"GETAWAY descriptors yielded better performance, with the linear regression model best in 9 out of 10 splits and significantly lower errors than WHIM-based models.\"}]","Machine learning for asphaltene polarizability - Evaluating molecular descriptors | PDF",1785729686,18,{"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-for-asphaltene-polarizability-evaluating-molecular-descriptors","",{"@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-for-asphaltene-polarizability-evaluating-molecular-descriptors/120365/",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 predicting asphaltene polarizability difficult?","Question",{"text":75,"@type":76},"Asphaltenes have strong structural heterogeneity across molecular architectures, which affects properties and makes polarizability harder to model reliably.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data splitting strategy and dataset size were used?",{"text":80,"@type":76},"The study analyzes 255 asphaltene structures using stratified sampling to create 10 independent training (80%) and testing (20%) splits.",{"name":82,"@type":73,"acceptedAnswer":83},"Which molecular descriptors produced better polarizability predictions?",{"text":84,"@type":76},"GETAWAY descriptors yielded better performance, with the linear regression model best in 9 out of 10 splits and significantly lower errors than WHIM-based models.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"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"]