Designing High-Performance Fuels through Graph Neural Networks for
Predicting Cetane Number of Multicomponent Surrogate Mixtures 2023-32-0052
Cetane number (CN) is an important fuel property in designing high-performance
fuels in recently diversifying compression ignition engines. We introduce graph
neural networks (GNNs) that predict CNs of multicomponent surrogate mixtures
when only 2D structures and mole fractions of molecules are given. It considers
the influences of mixing multiple components and their chemical structures on
CN, reproducing the non-linear blending behavior observed for certain mixtures.
We trained the GNNs using the CNs of 1,143 mixtures, and reliable accuracy was
achieved with mean absolute errors of 3.4-3.8 from the cross-validation. Lastly,
we analyzed the chemical structural effects on non-linear blending behavior.