Fig. 3

Graph neural network QSPR approaches for predicting viscosity. A Workflow of the graph neural network (GNN) based approaches using methyl acetate as an example. Methyl acetate is represented as a molecular graph (G) with atoms as nodes (V) and bonds as edges (E). B Five-fold cross validation and test set RMSE for QSPR models. The average RMSE is reported across five random train-test splits and the RMSE uncertainty is estimated by computing the standard deviation across the splits. LGBM is included in this plot as a comparison between the best descriptor-based QSPR model against GNN QSPR models. Only the top five performing GNNs are shown for brevity, which were selected based on Eq. 1. C Parity plot between predicted and actual log-viscosity showing the validation set predictions across 5-CV on the training set for a single train/test split when using the EdgePool model, which had the highest model score based on 5-CV and test set \(R^2\). Each color indicates the different validation sets for each of the five folds. The number of examples used (N), \(R^2\), and RMSE for 5-CV are reported within the plot. D Parity plot between predicted and actual log viscosity for a single 80:20 train:test split for the EdgePool model. The total number of examples used (N) and statistics (i.e. \(R^2\) and RMSE) for train and test sets are reported within the plot. For all parity plots, a dashed diagonal \(y = x\) line is drawn as a guide to indicate which predictions are in agreement with the actual values