Performance of q -ACSFs con f was fully asssessed over three distinct datasets through MAE and MSE. (Detailed definition of the criterion could be found in supporting information, Section S8) Five methods, namely, XLOGP3 , MolLogP 18 , ALOGPS 2.1 , JPlogP-Coeff and JPlogP-library were also applied for comparison. As listed in Table , it should be a hard task for these five methods to predict the partition coefficient in the datasets Martel, as the distribution of partition coefficient is quit different from the one we trained on (Figure ). However, among them, q -ACSFs con f outperforms these methods except for JPlogP-library, as the MAE and MSE could decreased to 0.91 and 1.50, respectively. While for the method MolLogP, the MAE and MSE are up to 1.06 and 1.93. Among the datasets Star & Non-Star, the performance of q -ACSFs con f is almost the same as the XLOGP3. The MAE and MSE predicted by q -ACSFs con f are only 0.22 and 0.12 over datasets Huuskonen, which is a great improvement with considering both polarization and entropy effects.