March 23, 2023 article Open access Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design Journal of Energy Resources Technology DOI: 10.1115/1.4062189 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 12 References 2 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 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 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 2 results