Our final applicability metric is based on the variance of the predictions of an ensemble of MPNNs. We created an ensemble by training 16 atomic contribution MPNNs each with a different, randomly-selected subset created by randomly sampling the full training set with replacement (i.e., a bootstrapped ensemble). The variance of this training captures the uncertainty of individual predictions. We assume that molecules with larger uncertainties are more likely to have larger errors.