Where AFProp(j, i) presents the aviation fuel compound property j and its descriptor i. As can be seen in Table , the three descriptors, NC, NH, and Num_of _Atoms, are dependent as NC+NH=Num_of_Atoms for hydrocarbons. Theoretically, orthogonal (independent) descriptors in Equ (1) are preferred because they simplify the interpretation of the model and reduce multicollinearity, which can lead to instability in the estimates of model coefficients. However, in practice, it's not always possible to have completely orthogonal descriptors. Sometimes, including non-orthogonal variables can improve the fitting for several reasons. For example, non-orthogonal variables might provide additional information that improves the model's predictive performance and reduces bias; they can also handle nonlinear relationships between the descriptors and the target variable, leading to more accurate predictions. Sometimes, domain-specific tasks need non-orthogonal variables that are known to be relevant to the prediction task, even if they are correlated with other variables. Moreover, Equ (1) can also be extended to other organic compounds rather than hydrocarbons. We are working on developing a new set of orthogonal descriptors for AF candidates, which are closely related to molecular structures in 3D and energies.