We next returned to the more challenging task of extrapolation to unseen structures using fine-tuned models. We retrained the initial models with more extensive hyperparameter tuning to maximize accuracy for this more challenging task. The already good performance of the initial ethylene model was not improved by further architecture variation, and, consequently, the previously identified hyperparameter values were left unchanged (Supporting Information Text S1 and Table ). On the other hand, the ethane model was found to improve significantly by incorporating dropout layers after each hidden layer, lowering the MAE from 1.11 to 0.75 mmol/g (Supporting Information Text S2, Table ). Comparison of training and test set performance for the ethane model still indicates significant overfitting, but even so we were able to achieve a moderate test set R 2 of over 0.60 (Figure ). We note that we attempted recursive feature addition to investigate if lowering the dimensionality of the feature space further improved model performance, but performance either remained unchanged or declined (Supporting Information Figure ). Model performance could be improved by further augmenting the dataset with more diverse MOF structures, but performance metrics show our models make accurate predictions even with the limited amount of training data available.