[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127773-en":3,"doc-seo-127773-105":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},127773,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Fluid Phase Equilibria - Machine learning coupled with group contribution for predicting the density of deep eutectic solvents - Manuscript Draft","Deep Eutectic Solvents (DESs) are positioned as green alternatives with favorable properties, requiring reliable physical-property prediction for chemical and related industrial applications. A comprehensive density dataset containing 1410 points across 166 DESs at various temperatures under atmospheric pressure was assembled from open literature to support improved modeling. Two machine learning methods—MLPANN and LSSVM—were developed together with the group contribution (GC) approach. The models incorporate 35 functional groups, temperature, and HBA/HBD molar ratios, and were evaluated using correlation and deviation metrics. The GC-enhanced models achieve strong predictive performance and outperform existing open-literature correlations and GC models, supported further by K-fold cross-validation for LSSVM.","Fluid Phase Equilibria  \nMachine learning coupled with group contribution for predicting the density of deep  \neutectic solvents  \n--Manuscript Draft--  \n\n| Manuscript Number: | FPE-D-22-00397R3 |\n| --- | --- |\n| Article Type: | VSI: Group contribution |\n| Keywords: | DES; Physical property; density; machine learning; group contribution |\n| Corresponding Author: | Sona Raeissi\u003Cbr>Shiraz University\u003Cbr>Shiraz, IRAN, ISLAMIC REPUBLIC OF |\n| First Author: | Ahmadreza Roosta |\n| Order of Authors: | Ahmadreza Roosta |\n|  | Reza Haghbakhsh |\n|  | Ana Rita C. Duarte |\n|  | Sona Raeissi |\n| Abstract: | Deep Eutectic Solvents (DESs) are a recently introduced class of green solvents with unique and favorable characteristics. Despite their recent debut, the scientific community has begun to place greater emphasis on them as alternatives to ionic liquids (ILs) . Knowledge of the various physical properties of DESs is essential for various applications in the chemical industries and related fields. In this study, a comprehensive database including 1410 density data points, from 166 different DESsat various temperatures and atmospheric pressure, were retrieved from open literature to develop models to increase the accuracy of density predictions. The densities of DESs were used to develop two commonly used machine learning models, namely Multilayer Perceptron Artificial Neural Network (MLPANN) and Least Square Support Vector Machine (LSSVM), in conjunction with the group contribution (GC) method. Based on the GC method, each fragment of a compound contributes a specific amount to the physical property’s value. By considering this, the prediction ability was improved by applying the GC method in the model development procedure. Both models predict the DES densities by taking into account the effect of 35 functional groups, the temperature, and the HBA/HBD molar ratios. The optimum MLPANN model structure consists of a single hidden layer with five neurons and a logarithmic sigmoid transfer function. By employing this MLPANN-GC model, the values of the squared correlation coefficient, R2, and absolute average relative deviation percent, AARD%, were 0.99 and 0.61%, respectively, while for the LSSVM-GC model (with the radial basis function (RBF) kernel), they were 0.99 and 0.56%, respectively. Also, K-fold cross-validation was used to assess the performance of the LSSVM-GC model. The presented machine learning models in this study were found to perform more accurately than those obtained using the best current correlations and GC models for DES densities in the open literature. The more accurate results, in addition to the enhanced predictability behavior of the developed models, give these models a preference for use in industrial and academic applications. |\n| Suggested Reviewers: | Ramesh Gardas\u003Cbr>Indian Institute of Technology Madras\u003Cbr>[gardas@iitm.ac.in](gardas@iitm.ac.in) |\n|  | Santiago Aparicio\u003Cbr>University of Burgos\u003Cbr>[sapar@ubu.es](sapar@ubu.es) |\n|  | Farouq Mjalli\u003Cbr>Sultan Qaboos University\u003Cbr>[farouqsm@squ.edu.om](farouqsm@squ.edu.om) |\n|  | Siddharth Pandey\u003Cbr>Indian Institute of Technology Delhi |\n\nPowered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation  \n\n|  | [sipandey@chemistry.iitd.ac.in](sipandey@chemistry.iitd.ac.in) |\n| --- | --- |\n|  | Jean Noël Jaubert\u003Cbr>UL National Graduate School of Chemical Engineering Library\u003Cbr>[jean-noel.jaubert@univ-lorraine.fr](jean-noel.jaubert@univ-lorraine.fr) |\n\nPowered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation  \nCover Letter  \nDear Prof. McCabe  \nEditor of Fluid Phase Equilibria  \nEnclosed, please find the revised version of our manuscript entitled “Machine learning coupled with group contribution for predicting the density of deep eutectic solvents”. The manuscript has been revised according to the suggestions and recommendations of the editor, and all changes have been highlighted in green in the revised version. A detailed ","cbCaig0ouHOSde0U","https://ap.wps.com/l/cbCaig0ouHOSde0U","pdf",4634410,1,99,"English","en",105,"# Abstract\n## Dataset construction\n## Machine learning models with group contribution\n## Model inputs and feature design\n## Performance metrics and validation\n## Comparative results and application outlook","[{\"question\":\"What dataset was used to build the density prediction models for deep eutectic solvents?\",\"answer\":\"A database of 1410 density measurements covering 166 different DESs was retrieved from open literature across various temperatures at atmospheric pressure.\"},{\"question\":\"Which machine learning models were combined with the group contribution method in the study?\",\"answer\":\"Two models were used: a Multilayer Perceptron Artificial Neural Network (MLPANN) and a Least Square Support Vector Machine (LSSVM), both coupled with the group contribution (GC) method.\"},{\"question\":\"What factors did the models consider when predicting DES density?\",\"answer\":\"The models accounted for 35 functional groups, temperature, and the HBA/HBD molar ratios, with the GC method mapping molecular fragments to property contributions.\"}]","Fluid Phase Equilibria - Machine learning coupled with group contribution for predicting the density of deep eutectic solvents - Manuscript Draft | PDF",1785941534,249,{"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},"fluid-phase-equilibria-machine-learning-coupled-with-group-contribution-for-predicting-the-density-of-deep-eutectic-solvents-manuscript-draft","",{"@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/fluid-phase-equilibria-machine-learning-coupled-with-group-contribution-for-predicting-the-density-of-deep-eutectic-solvents-manuscript-draft/127773/",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","2026-08-23","2026-08-05",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},"What dataset was used to build the density prediction models for deep eutectic solvents?","Question",{"text":76,"@type":77},"A database of 1410 density measurements covering 166 different DESs was retrieved from open literature across various temperatures at atmospheric pressure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were combined with the group contribution method in the study?",{"text":81,"@type":77},"Two models were used: a Multilayer Perceptron Artificial Neural Network (MLPANN) and a Least Square Support Vector Machine (LSSVM), both coupled with the group contribution (GC) method.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors did the models consider when predicting DES density?",{"text":85,"@type":77},"The models accounted for 35 functional groups, temperature, and the HBA/HBD molar ratios, with the GC method mapping molecular fragments to property contributions.","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},[94,98,102,106,111,116,121,124,129,132,136],{"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":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]