Model performance can be quantified based on a number of different parameters including R 2 score, root mean square error (RMSE), and mean absolute error (MAE). These metrics all capture different aspects of model performance. However, we have observed that the correlation coefficient (R) is occasionally reported instead of the R 2 score, and as the sole performance metric. This is not a robust metric since R only captures the linear correlation between true and predicted values while the R 2 score quantifies the deviation of the model prediction from the ground truth. However, while the Pearson R statistic is less strict in evaluating the model performance, a model with high R score may indicate modest success in training, and can still provide predictions that approximate the ground truth up to some correction term. In order to gain a better statistical understanding of the variability in model performance, cross-validation should be performed. This provides information on the variability of the model with different train and test sets. We achieve this by carrying out 5-fold cross-validation from seven independent random seeds which provide 35 different train and test set combinations. Given that the random seeds are independent, we can determine variability based on the standard error of the mean. Finally, it is also important to carefully apply feature scaling so as not to overestimate the model's performance. The training set features should be scaled first, ensuring a standardized feature space that the models can learn from, and then that scaling should be applied to the test set features. If all features are scaled together, this can result in data leakage between the train and test sets, implicitly encoding information about the train set into the test set, providing overestimates on the model performance. Linear methods are the simplest: they assume that there is some linear relationship between the input features and the target value, which can be used to predict the target value based on a linear combination of the input features. One of the greatest advantages to linear regression is the interpretability of the model: linear effects are easy to quantify, and to describe. As expected, multiple linear regression (MLR) is only usable with relatively low dimensional input data (material properties), where it achieves an R 2 score of 0.35±0.03. Linear models are not able to effectively handle binary (OHE, ECFP) or sequence-based (SMILES, SELFIES) features.