January 11, 2003 article Structural Group Contribution Method for Predicting the Octane Number of Pure Hydrocarbon Liquids Industrial & Engineering Chemistry Research DOI: 10.1021/ie020306+ OpenAlex Authors 0 Author list not loaded yet. Cited by 4 stored of 103 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Exploring the fuel structure dependence of laminar burning velocity: A machine learning based group contribution approach 2021 Combustion and Flame article Chemical Engineering Advanced Combustion Engine Technologies 62 citations A property database of fuel compounds with emphasis on spark-ignition engine applications 2020 Applications in Energy and Combustion Science article Chemical Engineering Advanced Combustion Engine Technologies Open access 48 citations Graph Neural Networks for Prediction of Fuel Ignition Quality 2020 Energy & Fuels article Materials Science Machine Learning in Materials Science 142 citations Using machine learning with target-specific feature sets for structure-property relationship modeling of octane numbers and octane sensitivity 2020 Fuel article Chemical Engineering Advanced Combustion Engine Technologies 60 citations 4 results References 0