We explored improving the performance of our model by adopting a "atomic contribution" model for solvation energy. As illustrated in Figure , our current model generates a single fingerprint to describe an entire molecule by summing the fingerprint of each atom (the output of the message passing layers) and computing the solvation energy from the resultant "molecular fingerprint." An alternative approach is to predict "atomic contributions" for each atom in the molecule separately and then to express the molecular property as a sum of these contributions-a technique used often in machine learning models of molecular energy. The atomic contribution approach explicitly encodes a linear relationship between a molecular property and the size of the molecule, which is a fair approximation for solvation energies given the known correlation between solvation energy and molecular size. The atomic contribution model performs worse for the QM9 holdout set but has a less dramatic increase in error with molecule size than our original model. As shown in Figure , the atomic contribution model has a 50% larger error on the QM9 dataset, 0.47 kcal/mol, than the molecular fingerprint model, 0.32 kcal/mol. However, we see a much smoother increase with error with increasing molecular size. The MAE for molecules with 14 nonhydrogen atoms is, for example, at least 5× smaller than our molecular fingerprint model. The drastic differences between the performance on QM9 and our external test sets strongly motivates the need for evaluating machine learning models more closely to how they will be used for larger molecules and the need for molecular datasets with larger molecules. We also note that the atomic contribution model is not fully free from the effect of molecular size on error. The errors steadily increase for molecules with sizes more distant from nine nonhydrogen atoms, which is the size with the greatest amount of training data. There could be a few explanations for the relationship between error and molecule size. Namely, there could be could be an effect of bias in the training set due to the overrepresentation of data with 9-heavy atoms or a failure in the assumption that atom count and solvation energy should be linearly-related. We therefore propose re-weighting techniques to account for training set bias or different approaches for summing atomic contributions (e.g., generalized means) as a direction for modeling non-linear relationships between atomic size and property.