[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290847-105":59,"doc-detail-290847-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","comparative-evaluation-of-machine-learningbased-qspr-techniques-for-predicting-polymer-glass-transition-temperature","Comparative evaluation of machine Learningbased QSPR techniques for predicting polymer glass transition temperature","","Glass transition temperature (Tg) is a key parameter for amorphous polymers and strongly affects their thermal behavior. The study combines machine learning with a cheminformatics framework to analyze Tg values across a broad polymer dataset and derive quantitative structure–property correlations. Using 250 polymers, the work builds ML-QSPR models with genetic algorithm and multiple linear regression for descriptor selection, then benchmarks nonlinear methods including SVR, GPR, random forest, and MLP.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparative-evaluation-of-machine-learningbased-qspr-techniques-for-predicting-polymer-glass-transition-temperature/290847/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparative-evaluation-of-machine-learningbased-qspr-techniques-for-predicting-polymer-glass-transition-temperature/290847.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is glass transition temperature (Tg) important for amorphous polymers?","Question",{"text":113,"@type":114},"Tg is a pivotal parameter that influences polymer behavior under different thermal properties, helping define material performance across temperatures and applications.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What machine learning models were compared in the study?",{"text":118,"@type":114},"The research compares genetic algorithm with multiple linear regression for descriptor optimization, then uses nonlinear methods including SVR, Gaussian process regression, random forest, and multi-layer perceptron for predictive analysis.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model achieved the best predictive accuracy for Tg?",{"text":122,"@type":114},"The multi-layer perceptron (MLP) model driven by 17 selected descriptors delivered the most accurate results, with training and external validation R2 values reported as 0.82 and 0.79.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290847,1789642784,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":26,"preview_page_count":81,"preview_urls":145},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Keya et al. Discover Materials (2026) 6:154 [https://doi.org/10.1007/s43939-026-00587-6](https://doi.org/10.1007/s43939-026-00587-6)  \nDiscover Materials  \nRESEARCH Open Access  \nComparative evaluation of machine Learningbased QSPR techniques for predicting polymer glass transition temperature  \nKamrun N. Keya 1,2, Amirreza Daghighi2,3, Gerardo M. Casanola-Martin2, Wenjie Xia1* and Bakhtiyor Rasulev2*  \n*Correspondence: Wenjie Xia[wxia@iastate.edu](wxia@iastate.edu)  \nBakhtiyor Rasulev [bakhtiyor.rasulev@ndsu.edu](bakhtiyor.rasulev@ndsu.edu)[ ](bakhtiyor.rasulev@ndsu.edu)1Department of Aerospace Engineering, Iowa State University, Ames, IA 50011, USA 2Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58108, USA  \n3Biomedical Engineering Program, North Dakota State University, Fargo, ND 58108, USA  \nAbstract  \nThe glass transition temperature ( Tg ) is a pivotal parameter for amorphous polymers, influencing their behavior under different thermal properties. Assessing Tg is crucial for evaluating material performance across varying temperatures. Our research integrates machine learning with cheminformatics framework to analyze Tg values across a broad polymer dataset. This framework improves a comprehensive understanding of the quantitative correlations between the polymers’ structural attributes and their Tg. Utilizing a dataset of 250 polymers, we constructed a series of Machine Learning-based Quantitative Structure-Property Relationship (ML-QSPR) models. The initial ML-QSPR used Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods to identify an optimal set of molecular descriptors. Subsequently, various non-linear machine learning methods, including Support Vector Regression (SVR), Gaussian Process Regression (GPR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were used for comparative purposes and predictive analysis. The results demonstrate that MLP, driven by seventeen (17) selected descriptors, yields the most accurate ML model, with training and external validation R2 values of 0.82 and 0. 79, respectively. This model predicts with good performance the Tg values of the polymers under study, showing the role of specific structural descriptors in refining polymer property predictions.  \nKeywords Glass transition temperature, Cheminformatics, Machine learning, QSPR, Artificial neural networks  \n1 Introduction  \nPolymers are used in several industries, including aerospace and biomedical applications, so knowing the material’s performance at different temperatures is essential. In polymer science, it is crucial to comprehend the behavior of polymers under various thermal conditions. That’s why it is crucial to accurately predict the thermal properties, such as the polymer’s glass transition temperature ( Tg ), which can help scientists to understand the polymer’s material performance [1, 2]. To determine Tg scientists usually actively use traditional experimental methods which are time consuming, high cost, and impractical for testing of large polymer libraries [3]. To address these challenges,  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use","cbCaisg9LFVYhomy","https://ap.wps.com/l/cbCaisg9LFVYhomy","pdf",3873868,24,"English","# Abstract\n## 1 Introduction\n## Research background and motivation\n## Machine learning and QSPR approach\n## Dataset and modeling strategy\n## Model comparison and performance results\n## Keywords","[{\"question\":\"Why is glass transition temperature (Tg) important for amorphous polymers?\",\"answer\":\"Tg is a pivotal parameter that influences polymer behavior under different thermal properties, helping define material performance across temperatures and applications.\"},{\"question\":\"What machine learning models were compared in the study?\",\"answer\":\"The research compares genetic algorithm with multiple linear regression for descriptor optimization, then uses nonlinear methods including SVR, Gaussian process regression, random forest, and multi-layer perceptron for predictive analysis.\"},{\"question\":\"Which model achieved the best predictive accuracy for Tg?\",\"answer\":\"The multi-layer perceptron (MLP) model driven by 17 selected descriptors delivered the most accurate results, with training and external validation R2 values reported as 0.82 and 0.79.\"}]","Comparative evaluation of machine Learningbased QSPR techniques for predicting polymer glass transition temperature | PDF",[146,150,154],{"page_no":8,"url":147,"width":148,"height":149},"https://res-academy.cache.wpscdn.com/upload/kdocshare/2026-11-08/12100fbdc0c6c1c9620a3a11ec33f1e4.webp",983,1310,{"page_no":14,"url":151,"width":152,"height":153},"https://res-academy.cache.wpscdn.com/upload/kdocshare/2026-11-08/5a8dbd1038f2b21abb32240cb1e9ed5d.webp",982,1309,{"page_no":81,"url":155,"width":156,"height":157},"https://res-academy.cache.wpscdn.com/upload/kdocshare/2026-11-08/5f605f7139f3cacc486d0d2f211ca51b.webp",981,1308]