We first discuss model bias errors due to the number of datapoints, using models trained with the d-MPNN. Fig. , top left, depicts the model performance as a function of training set size and model size (size of hidden layers in the message passing and feed-forward networks). For a model of a given number of parameters, increasing the number of datapoints increases the accuracy of the model's predictions, where the slope on a log-log plot is nearly independent of the number of parameters. The error reduction with more data is presumptively data coverage error, but where is this data coverage error coming from? Is it caused by model bias, where a low number of datapoints does not allow the model to find the true global minimum in the high-dimensional parameter space? Or is it caused by variance, where differently initialized models converge to a distribution of model outcomes with associated random variations in observed error? Our recent work applying Bayesian inference to ensembling uses the observed variation in prediction error within an ensemble to estimate the distribution of errors before variance is applied, i.e. the nonvariance component of the error. Using this method, we decompose the total error into contributions from variance and bias (here, bias is computed as all error not from variance, which is a valid assumption for a noiseless dataset). The mean absolute errors shown in Fig. are for single models, though the inference of the bias and variance contributions was made using the distribution of predictions observed in ensembles of 5 submodels.