[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123843-en":3,"doc-seo-123843-105":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},123843,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Descriptors-based machine-learning prediction of cetane number using quantitative structure-property relationship","Liquid alternative fuels require reliable physicochemical property estimation, yet measurement and prediction become challenging when complex surrogate fuels are involved. This study builds quantitative structure-property relationship models using machine learning, where fuel chemical structure is encoded with molecular descriptors to connect composition features to key fuel utilization properties. Feature selection identifies the most relevant descriptors, multiple algorithms are evaluated for interpretable modeling, and predictive performance is demonstrated for cetane numbers. Matrix-based descriptors and atom-count-related descriptors directly relate to cetane number for hydrocarbons, while molecular connectivity indices influence aromatic molecules; the framework is extended to ester and ether molecules to support sustainable fuel design.","Graphical Abstract  \nDescriptors-based machine-learning prediction of cetane number using quantitative structure-property relationship  \nRodolfo S. M. Freitas, Xi Jiang  \nHighlights  \nDescriptors-based machine-learning prediction of cetane number using quantitative structure-property relationship  \nRodolfo S. M. Freitas, Xi Jiang  \n• Machine learning models were employed to predict the cetane number of hydrocarbon fuels from the chemical structures.  \n• A feature selection approach is used to select the most relevant molecular descriptors that describe the chemical structure of the fuel.  \n• Impact of the descriptors on cetane number predictions is evaluated using the SHAP method  \n• The models showed appreciable predictability and reproducibility, returning a mean absolute error of 5 .07.  \nDescriptors-based machine-learning prediction of cetane number using quantitative structure-property  \nrelationship  \nRodolfo S. M. Freitasa , Xi Jianga  \na School of Engineering and Materials Science, Queen Mary University of London, Mile  \nEnd Road, London, E1 4NS, UK  \nAbstract  \nThe physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure-property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrixbased descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels toward fully sustainable fuel utilization.  \nKeywords: Chemical descriptors, Quantitative Structure-Property relationship, Machine Learning, Cetane number, Fuel design  \nPreprint submitted to Energy and AI June 6, 2024  \n1 1. Introduction  \n2 Low-carbon alternative fuels are becoming increasingly important, but  \n3 fossil fuels still play a key role in energy supply, especially in difficult-to- 4 decarbonize transport applications such as shipping, road freight, and avi- 5 ation transport. Overall, they are responsible for emitting more than 50%  \n6 CO2 of the entire transport sector [1] . With the need to take a step towards  \n7 net zero emissions and sustainable energy utilization, renewable fuels and  \n8 biofuels derived from sources other than petroleum are becoming increas- 9 ingly important [2] . The design of alternative fuels is often based on the  \n10 life cycle assessment methodology, which is an all-encompassing evaluation  \n11 method employed to estimate fuel viability and benefits, offering insights  \n12 into their environmental impact, energy efficiency, economic feasibility, and  \n13 sustainable decision-making through a holistic evaluation [3] .  \n14 In light of the fuel design, one of the essential “fuel criteria” is that an al- 15 ternative fuel must be compatible with the existing global infrastructure [4] .  \n16 So, it can be integrated into the current transportation system using exist- 17 ing infrastructure and be burned in existing engines (such as diesel engines 18 for optimal fuel economy) with minor adjustments as drop-in fuels. Among  \n19 efforts on de","cbCaij4ea17YOb5W","https://ap.wps.com/l/cbCaij4ea17YOb5W","pdf",2444984,1,49,"English","en",105,"# Introduction\n## Low-carbon alternative fuels and fuel design\n## Liquid synthetic fuels and OMEx background\n## Study motivation and objective","[{\"question\":\"How are molecular descriptors used in the prediction of cetane number?\",\"answer\":\"The fuel chemical structure is represented by molecular descriptors, enabling the models to link structural composition features to cetane number-related fuel properties.\"},{\"question\":\"What role does feature selection play in the methodology?\",\"answer\":\"Feature selection selects the most relevant molecular descriptors that describe the fuel’s chemical structure, improving the effectiveness and interpretability of the resulting models.\"},{\"question\":\"How is interpretability of descriptor impact evaluated?\",\"answer\":\"The study evaluates the impact of descriptors on predictions using the SHAP method to understand how molecular features contribute to cetane number estimates.\"}]","Descriptors-based machine-learning prediction of cetane number using quantitative structure-property relationship | 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are molecular descriptors used in the prediction of cetane number?","Question",{"text":75,"@type":76},"The fuel chemical structure is represented by molecular descriptors, enabling the models to link structural composition features to cetane number-related fuel properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does feature selection play in the methodology?",{"text":80,"@type":76},"Feature selection selects the most relevant molecular descriptors that describe the fuel’s chemical structure, improving the effectiveness and interpretability of the resulting models.",{"name":82,"@type":73,"acceptedAnswer":83},"How is interpretability of descriptor impact evaluated?",{"text":84,"@type":76},"The study evaluates the impact of descriptors on predictions using the SHAP method to understand how molecular features contribute to cetane number 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