Due to their density and complexity, deep learning models can be susceptible to particularly high variance. Especially, when trained on a small dataset, there is inherent stochasticity resulting from the network's initialization and the order of data processing during training. There exist many ways to quantify and reduce uncertainty for problems with chemical inputs . One simple way to improve the predictive capacity of such models is through ensembling, or averaging together several models trained under different conditions. For example, given an ensemble of distinct models ℰ 𝑀 , 𝑀 , … , 𝑀 and inputs 𝑥, the ensemble prediction is given by the mean of all the model predictions