A big database containing molecular structures and multiple properties of 2899 hydrocarbon molecules(the number of carbon atom is from 1 to 50),was constructed via data collection,structure optimization and quantum chemistry calculation.Seven properties were focused,including melting point(Tm),boiling point(Tb),density(ρ),internal energy at 0 K(U0),inter-nal energy at 298.15 K(U),enthalpy at 298.15 K(H)and Gibbs free energy at 298.15 K(G).Four different machine learning models were established,including Decision Tree Regressor,Lasso CV,Ridge CV and XGBoost Regressor,using coulomb ma-trix representing molecular structures as the input.In comparison,the XGBoost Regressor model is more suitable for regressing experimental melting point,boiling point and density of hydrocarbon molecules;Ridge CV model is more suitable for the predic-tion of four thermodynamic energy properties.In addition,the optimized machine learning combined model can accurately pre-dict the properties of the hydrocarbon molecules with same carbon numbers,hydrocarbons with different types and hydrocarbon isomers.Furthermore,the densities of 34 high-density hydrocarbon fuels reported experimentally were calculated by the opti-mized machine learning model.The mean absolute error between the calculated values and the experimental values is only 0.0290 g cm-3.Next,the fuel properties of 319,893 hydrocarbon molecules in GDB-13C were predicted by the machine learn-ing model to establish a big database containing hydrocarbon structure and fuel properties.Based on high-throughput screening,37 hydrocarbon fuel molecules with low freezing point and high density have been discovered.Through the proof-of-concept via group contribution method and DFT method,the net heat of combustion and specific impulse of the as-screened new molecules are similar to those of JP-10 and quadricyclane(QC).
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