This work examines methods for predicting the partition coecient (log P) for a dataset of small molecules. Here, we use atomic attributes such as radius and partial charge, which are typically used as forcefield parameters in classical molecular dynamics simulations. These atomic features are transformed into index-invariant molecular features using a recently developed method called Geometric Scattering for Graphs (GSG). We call this approach "ClassicalGSG" and examine its performance under a broad range of conditions and hyperparameters. We train a ClassicalGSG log P predictor with neural networks using 10722 molecules from the ChEMBL21 dataset and apply it to predict the log P values from four independent test sets. The ClassicalGSG method's performance is compared to a baseline model that employs graph convolutional neural networks (GCNNs). Our results show that the best prediction accuracies are obtained using atomic attributes generated with the CHARMM generalized Force Field (CGenFF) and 2D molecular structures.